Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks | Paper Citation Record

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This paper cites Adversarial ma- chine learning: A taxonomy and terminology of attacks and mitigations,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Adversarial ma- chine learning: A taxonomy and terminology of attacks and mitigations,

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This paper cites Explaining and Harnessing Adversarial Examples.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Explaining and Harnessing Adversarial Examples

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This paper cites BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

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This paper cites Manipulating machine learning: Poisoning attacks and countermeasures for regression learning,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Manipulating machine learning: Poisoning attacks and countermeasures for regression learning,

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This paper cites Adversarial machine learning- industry perspectives,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Adversarial machine learning- industry perspectives,

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This paper cites Nic: Detecting adversarial samples with neural network invariant checking,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Nic: Detecting adversarial samples with neural network invariant checking,

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This paper cites What you see is not what the network infers: detecting adversarial examples based on semantic contradiction,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks What you see is not what the network infers: detecting adversarial examples based on semantic contradiction,

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This paper cites STRIP: A defence against trojan attacks on deep neural networks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks STRIP: A defence against trojan attacks on deep neural networks,

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Observation a7945b7b-da6e-465e-8ca9-a00ab4b843f3 · outbound

This paper cites Robust backdoor detection for deep learning via topological evolution dynamics,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Robust backdoor detection for deep learning via topological evolution dynamics,

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This paper cites Backdoor Secrets Unveiled: Identifying Backdoor Data with Optimized Scaled Prediction Consistency.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Backdoor Secrets Unveiled: Identifying Backdoor Data with Optimized Scaled Prediction Consistency

Reference 10

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This paper cites Dataset security for machine learn- ing: Data poisoning, backdoor attacks, and defenses,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Dataset security for machine learn- ing: Data poisoning, backdoor attacks, and defenses,

Reference 11

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This paper cites Adversarial neuron pruning purifies backdoored deep models,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Adversarial neuron pruning purifies backdoored deep models,

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This paper cites Gotta catch’em all: Using honeypots to catch adversarial attacks on neural networks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Gotta catch’em all: Using honeypots to catch adversarial attacks on neural networks,

Reference 13

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This paper cites AI-guardian: Defeating adversarial attacks using backdoors,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks AI-guardian: Defeating adversarial attacks using backdoors,

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This paper cites Towards unified robustness against both backdoor and adversarial attacks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Towards unified robustness against both backdoor and adversarial attacks,

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This paper cites A tale of evil twins: Adversarial inputs versus poisoned models,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks A tale of evil twins: Adversarial inputs versus poisoned models,

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This paper cites On model outsourcing adaptive attacks to deep learning backdoor defenses,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks On model outsourcing adaptive attacks to deep learning backdoor defenses,

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This paper cites Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent

Reference 18

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This paper cites Feature squeezing: Detecting adversarial examples in deep neural networks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Feature squeezing: Detecting adversarial examples in deep neural networks,

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This paper cites Addition: Detecting adversarial examples with image-dependent noise reduction,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Addition: Detecting adversarial examples with image-dependent noise reduction,

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This paper cites DISCO: Adversarial defense with local implicit functions,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks DISCO: Adversarial defense with local implicit functions,

Reference 21

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This paper cites {PatchCleanser}: Certifiably robust defense against adversarial patches for any image classifier,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks {PatchCleanser}: Certifiably robust defense against adversarial patches for any image classifier,

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This paper cites SentiNet: Detecting localized universal attacks against deep learning systems,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks SentiNet: Detecting localized universal attacks against deep learning systems,

Reference 23

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This paper cites Scale-up: An efficient black-box input-level backdoor detection via analyzing scaled prediction consistency,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Scale-up: An efficient black-box input-level backdoor detection via analyzing scaled prediction consistency,

Reference 24

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This paper cites Towards universal detection of adversarial examples via pseudorandom classifiers,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Towards universal detection of adversarial examples via pseudorandom classifiers,

Reference 25

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This paper cites MM-BD: Post-training detection of backdoor attacks with arbitrary backdoor pattern types using a maximum margin statistic,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks MM-BD: Post-training detection of backdoor attacks with arbitrary backdoor pattern types using a maximum margin statistic,

Reference 26

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This paper cites Demon in the variant: Sta- tistical analysis of DNNs for robust backdoor contamination detection,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Demon in the variant: Sta- tistical analysis of DNNs for robust backdoor contamination detection,

Reference 27

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This paper cites The" beatrix.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks The" beatrix

Reference 28

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This paper cites NTD: Non-transferability enabled deep learning backdoor detection,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks NTD: Non-transferability enabled deep learning backdoor detection,

Reference 29

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This paper cites Deep one-class classification,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Deep one-class classification,

Reference 30

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This paper cites Uniform manifold approximation and pro- jection,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Uniform manifold approximation and pro- jection,

Reference 31

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Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Trojaning attack on neural networks,

Reference 32

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This paper cites Ad- versarial example detection for dnn models: A review and experimental comparison,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Ad- versarial example detection for dnn models: A review and experimental comparison,

Reference 33

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This paper cites Reducing excessive margin to achieve a better accuracy vs. robustness trade-off,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Reducing excessive margin to achieve a better accuracy vs. robustness trade-off,

Reference 34

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This paper cites Towards deep learning models resistant to adversarial attacks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Towards deep learning models resistant to adversarial attacks,

Reference 35

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This paper cites Robust learning meets generative models: Can proxy distributions improve adversarial robustness?.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Robust learning meets generative models: Can proxy distributions improve adversarial robustness?

Reference 36

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This paper cites On the (Statistical) Detection of Adversarial Examples.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks On the (Statistical) Detection of Adversarial Examples

Reference 37

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This paper cites A study of the effect of JPG compression on adversarial images.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks A study of the effect of JPG compression on adversarial images

Reference 38

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This paper cites Feature distillation: Dnn-oriented jpeg compression against adversarial examples,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Feature distillation: Dnn-oriented jpeg compression against adversarial examples,

Reference 39

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Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Countering Adversarial Images using Input Transformations

Reference 40

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This paper cites Deflecting adversarial attacks with pixel deflection,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Deflecting adversarial attacks with pixel deflection,

Reference 41

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This paper cites Mitigating Adversarial Effects Through Randomization.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Mitigating Adversarial Effects Through Randomization

Reference 42

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This paper cites Defense-gan: Protecting classifiers against adversarial attacks using generative models,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Defense-gan: Protecting classifiers against adversarial attacks using generative models,

Reference 43

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This paper cites Eliminating adversarial noise via information discard and robust representation restoration,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Eliminating adversarial noise via information discard and robust representation restoration,

Reference 44

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This paper cites Diffusion models for adversarial purification,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Diffusion models for adversarial purification,

Reference 45

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This paper cites DiffSmooth: Certifiably robust learning via diffusion models and local smoothing,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks DiffSmooth: Certifiably robust learning via diffusion models and local smoothing,

Reference 46

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This paper cites Characterizing adversarial subspaces using local intrinsic dimensionality,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Characterizing adversarial subspaces using local intrinsic dimensionality,

Reference 47

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This paper cites Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning

Reference 48

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This paper cites Detecting Adversarial Samples from Artifacts.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Detecting Adversarial Samples from Artifacts

Reference 49

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This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks A simple unified framework for detecting out-of-distribution samples and adversarial attacks,

Reference 50

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This paper cites Adversarial example detection using latent neighborhood graph,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Adversarial example detection using latent neighborhood graph,

Reference 51

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Observation e5c4e9c8-791b-4b02-aa7d-2e33cc7720f1 · outbound

This paper cites Towards certifiable adversarial sample detection,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Towards certifiable adversarial sample detection,

Reference 52

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Observation 9a1b9741-47e3-4964-868d-07d59677b7d5 · outbound

This paper cites Objectseeker: Certifiably robust object detection against patch hiding attacks via patch- agnostic masking,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Objectseeker: Certifiably robust object detection against patch hiding attacks via patch- agnostic masking,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:51.696143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e909bcdb-8e25-4dd3-8e08-bfd75e0bf351 · outbound

This paper cites CAS- SOCK: Viable backdoor attacks against DNN in the wall of source- specific backdoor defences,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks CAS- SOCK: Viable backdoor attacks against DNN in the wall of source- specific backdoor defences,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:51.563088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cfbdcfeb-d7f5-4723-a2c3-846b8ea87bb5 · outbound

This paper cites Neural Cleanse: Identifying and mitigating backdoor attacks in neural networks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Neural Cleanse: Identifying and mitigating backdoor attacks in neural networks,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:51.442155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 32f9cc7e-0450-4f2b-9110-ddc18ed7b8a6 · outbound

This paper cites Watch out! simple horizontal class backdoor can trivially evade defense,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Watch out! simple horizontal class backdoor can trivially evade defense,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:51.326576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2dc890ca-3687-4c57-acd7-b217d79f15cc · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T22:06:40.384122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4561f514-ddbb-486f-9fa9-4282b8b0e3b7 · outbound

This paper cites WaNet -- Imperceptible Warping-based Backdoor Attack.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks WaNet -- Imperceptible Warping-based Backdoor Attack

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T22:06:40.498394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d11879e2-ba97-4bce-8eae-b9892e983b6a · outbound

This paper cites Invisible backdoor attack with sample-specific triggers,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Invisible backdoor attack with sample-specific triggers,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:51.217450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a68c69df-27c4-4925-a006-89d72fb39be0 · outbound

This paper cites Anti-backdoor learning: Training clean models on poisoned data,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Anti-backdoor learning: Training clean models on poisoned data,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:51.073595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c164ec78-ea2c-4377-bc20-4f306c1fd46c · outbound

This paper cites Model orthogonalization: Class distance hardening in neural networks for better security,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Model orthogonalization: Class distance hardening in neural networks for better security,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:50.938713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ae189195-d7bf-4b43-b2c9-dcda82cef7de · outbound

This paper cites Backdoor defense via decoupling the training process,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Backdoor defense via decoupling the training process,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:50.802170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7989b32c-3dd6-462a-bcd6-3d736ca03ef6 · outbound

This paper cites REDEEM MYSELF: Purifying backdoors in deep learning models using self attention distillation,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks REDEEM MYSELF: Purifying backdoors in deep learning models using self attention distillation,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:50.597232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0d6f51fd-05ed-41d2-a6f2-da8eae958409 · outbound

This paper cites Selective amnesia: On efficient, high-fidelity and blind suppression of backdoor effects in trojaned machine learning models,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Selective amnesia: On efficient, high-fidelity and blind suppression of backdoor effects in trojaned machine learning models,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:50.424882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5762d932-cb5e-490f-b840-2c58cd5d1602 · outbound

This paper cites Fine-pruning: Defending against backdooring attacks on deep neural networks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Fine-pruning: Defending against backdooring attacks on deep neural networks,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:50.284269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 119fbae6-d716-4e66-8b7e-d5c095284e95 · outbound

This paper cites Black-box backdoor defense via zero-shot image purification,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Black-box backdoor defense via zero-shot image purification,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:50.079583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cfbd81da-70b7-474b-b462-5995759b03fd · outbound

This paper cites Black- box detection of backdoor attacks with limited information and data,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Black- box detection of backdoor attacks with limited information and data,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:49.891981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6a018c8c-5ed6-4c9d-9dc5-26974166eec9 · outbound

This paper cites Towards inspecting and eliminating trojan backdoors in deep neural networks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Towards inspecting and eliminating trojan backdoors in deep neural networks,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:49.698062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b0c2a86a-b165-4b0e-9d84-6aa4c5a6fcde · outbound

This paper cites ABS: Scanning neural networks for backdoors by artificial brain stimulation,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks ABS: Scanning neural networks for backdoors by artificial brain stimulation,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:49.545881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1c6de3ea-5499-47e2-a05c-cfbd6a16e055 · outbound

This paper cites DeepInspect: A black-box Trojan detection and mitigation framework for deep neural networks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks DeepInspect: A black-box Trojan detection and mitigation framework for deep neural networks,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:49.372386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 111344f2-4278-48c4-a8b1-666cb34dc968 · outbound

This paper cites Detecting AI trojans using meta neural analysis,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Detecting AI trojans using meta neural analysis,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:49.175956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9341b370-8be4-4881-b2b2-9affb48036af · outbound

This paper cites Universal litmus patterns: Revealing backdoor attacks in CNNs,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Universal litmus patterns: Revealing backdoor attacks in CNNs,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:48.960404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cda37c90-4ccf-4b2f-b5dd-e66f749f69d1 · outbound

This paper cites Trojan signatures in DNN weights,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Trojan signatures in DNN weights,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:48.807138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b729dd9a-7d54-41a8-849a-75d49fd24bd8 · outbound

This paper cites Try to poison my deep learning data? nowhere to hide your trajectory spectrum!.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Try to poison my deep learning data? nowhere to hide your trajectory spectrum!

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:48.591344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f7d53cff-c43b-44dc-9065-caa32671a3cd · outbound

This paper cites Towards a proactive ML approach for detecting backdoor poison samples,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Towards a proactive ML approach for detecting backdoor poison samples,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:48.429094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 68a23d9e-3c81-4a03-b794-6f88e1f9e1d1 · outbound

This paper cites ASSET: Robust backdoor data detection across a multiplicity of deep learning paradigms,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks ASSET: Robust backdoor data detection across a multiplicity of deep learning paradigms,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:48.255650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 341bfc5f-47dd-4296-84cd-a6e2a3dbbc5f · outbound

This paper cites Support vector data description,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Support vector data description,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:48.028463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 76cd160e-8e7b-4762-966e-97ebb853b256 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T22:06:42.948346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4b09f8b-4da9-4d9e-8411-3d89e4870d71 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T22:06:43.067827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e735ed33-bd8b-49d9-9e65-58f36730c6e0 · outbound

This paper cites Adversarial examples in the physical world,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Adversarial examples in the physical world,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:47.819341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d4d3303f-6918-4d3b-aa86-dbb802735819 · outbound

This paper cites Towards evaluating the robustness of neural networks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Towards evaluating the robustness of neural networks,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:47.633986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 29ab1df8-2b4c-4d6f-b75b-885dba17259f · outbound

This paper cites Deepfool: a simple and accurate method to fool deep neural networks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Deepfool: a simple and accurate method to fool deep neural networks,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:47.404017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7efdb8e2-2d27-47b2-bd01-4e86e3475933 · outbound

This paper cites The limitations of deep learning in adversarial settings,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks The limitations of deep learning in adversarial settings,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:47.141552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f7a3dee5-a533-4b7f-a56a-aba4b72d0ff9 · outbound

This paper cites Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T22:06:43.758930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41b8b22b-3377-4ef0-ba22-5a65d096a6a3 · outbound

This paper cites Adversarial machine learning in image classification: A survey toward the defender’s per- spective,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Adversarial machine learning in image classification: A survey toward the defender’s per- spective,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:46.910653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c9b475f7-93cc-490b-87bc-41f570b6c560 · outbound

This paper cites Deep residual learning for image recognition,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Deep residual learning for image recognition,

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T22:06:43.952055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83ef3df7-0209-4026-a03b-767510a33192 · outbound

This paper cites Tiny imagenet visual recognition challenge,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Tiny imagenet visual recognition challenge,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T22:06:44.047422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e29c732-7d74-4832-b6c3-ad018d6c9ea2 · outbound

This paper cites AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T22:06:44.145779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d38c0022-ed5a-464e-834c-5bdce4a6e10d · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Recursive deep models for semantic compositionality over a sentiment treebank,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:46.717729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c4aa5016-9adc-448a-98a3-5e590ca29090 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T22:06:44.351097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e39b5cc0-4a9d-4a59-9338-9bab129e0f41 · outbound

This paper cites A comprehensive survey on pretrained foundation models: A history from bert to chatgpt,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks A comprehensive survey on pretrained foundation models: A history from bert to chatgpt,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:46.495935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation da345400-6487-4735-a849-9cc8aaecde13 · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-06T22:06:44.505386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca82e6cb-9b70-47d4-8416-39e442e0298b · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks LoRA: Low-Rank Adaptation of Large Language Models

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-06T22:06:44.604934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44284909-3a4f-425b-8a0c-1ebb9eedc2b9 · outbound

This paper cites Generating natural language adversarial examples through probability weighted word saliency,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Generating natural language adversarial examples through probability weighted word saliency,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:46.339350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2d6d0482-bceb-4450-93e0-0aded82b5fcc · outbound

This paper cites Textbugger: Generating adversarial text against real-world applications,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Textbugger: Generating adversarial text against real-world applications,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:46.116722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b5664576-4422-4fc7-8d1f-42f7dace659f · outbound

This paper cites Apparent and real age estimation in still images with deep residual regressors on appa-real database,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Apparent and real age estimation in still images with deep residual regressors on appa-real database,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:45.918030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8d159858-70b1-4c89-9a29-d6fd1a15ae2b · outbound

This paper cites Aggregated residual transformations for deep neural networks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Aggregated residual transformations for deep neural networks,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:45.714176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 98b3b073-a80f-4f6a-bf99-9c8e840b9a49 · outbound

This paper cites Rab: Provable robustness against backdoor attacks,.

Kill Two Birds with One Stone! Trajectory enabled Unified Online Detection of Adversarial Examples and Backdoor Attacks Rab: Provable robustness against backdoor attacks,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:06:45.505081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers