Behavior Backdoor for Deep Learning Models | Paper Citation Record

Observation ff676e60-7f45-40ee-8f97-f66ed17fb657 · outbound

This paper cites Struc- tured pruning of deep convolutional neural networks.

Behavior Backdoor for Deep Learning Models Struc- tured pruning of deep convolutional neural networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:22.569642Z

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Observation 2f690827-1390-486f-90dc-764a08c3141d · outbound

This paper cites Medical image segmentation review: The suc- cess of u-net.

Behavior Backdoor for Deep Learning Models Medical image segmentation review: The suc- cess of u-net

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.779548Z

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Observation 547de60b-c5ef-4dcb-8d4d-b970b7e24725 · outbound

This paper cites A new backdoor attack in cnns by training set corruption without label poisoning.

Behavior Backdoor for Deep Learning Models A new backdoor attack in cnns by training set corruption without label poisoning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:22.493993Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4162d11f-797f-4dd7-80eb-a7b7060e6f5b · outbound

This paper cites Review of image classification algorithms based on convolutional neural networks.

Behavior Backdoor for Deep Learning Models Review of image classification algorithms based on convolutional neural networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:22.468802Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 595da81a-a571-48fe-9216-386b11c3867a · outbound

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

Behavior Backdoor for Deep Learning Models Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.795851Z

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Observation a898f3bb-625d-49d5-8297-ba61848b3267 · outbound

This paper cites A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets.

Behavior Backdoor for Deep Learning Models A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.802268Z

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Observation 6b61e255-9a4d-4249-b2ac-c334c6eaf66e · outbound

This paper cites The mnist database of handwritten digit images for machine learning research [best of the web].

Behavior Backdoor for Deep Learning Models The mnist database of handwritten digit images for machine learning research [best of the web]

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:22.448163Z

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Observation 4c771c0b-84f7-4638-8f5f-633b1513cf5c · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Behavior Backdoor for Deep Learning Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.814737Z

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Observation bd09c833-a36a-467f-a349-1743e3922b26 · outbound

This paper cites The pascal visual object classes (voc) challenge.

Behavior Backdoor for Deep Learning Models The pascal visual object classes (voc) challenge

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.820917Z

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Observation 009d982c-ca6c-4f36-9908-939eea2c9fe6 · outbound

This paper cites Depgraph: Towards any structural pruning.

Behavior Backdoor for Deep Learning Models Depgraph: Towards any structural pruning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:22.396194Z

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Observation dfc6540e-2ffe-4e4a-8a0e-a13174465311 · outbound

This paper cites Privacy Backdoors: Stealing Data with Corrupted Pretrained Models.

Behavior Backdoor for Deep Learning Models Privacy Backdoors: Stealing Data with Corrupted Pretrained Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.834898Z

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Observation c7e6cb3f-b89f-4290-abc8-03e2e9781145 · outbound

This paper cites Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review.

Behavior Backdoor for Deep Learning Models Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.842379Z

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Observation e329991e-7e63-4b69-a444-b22e9abadee7 · outbound

This paper cites A survey of quan- tization methods for efficient neural network inference.

Behavior Backdoor for Deep Learning Models A survey of quan- tization methods for efficient neural network inference

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:22.359636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c67a5a2d-7edb-4b4b-b6bc-21997f12e035 · outbound

This paper cites Badnets: Evaluating backdooring attacks on deep neu- ral networks.

Behavior Backdoor for Deep Learning Models Badnets: Evaluating backdooring attacks on deep neu- ral networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:22.272881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 44a31662-826a-48f8-a5f2-490b41436a40 · outbound

This paper cites Optimal brain surgeon and general network pruning.

Behavior Backdoor for Deep Learning Models Optimal brain surgeon and general network pruning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.862568Z

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Observation 64b6666a-a8e0-4b4c-925f-2c4c909b4c53 · outbound

This paper cites Zhang, Shaoqing Ren, and Jian Sun.

Behavior Backdoor for Deep Learning Models Zhang, Shaoqing Ren, and Jian Sun

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:22.167000Z

Source-reported events for the cited work

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Observation 17c9309d-5333-4a74-9aeb-da7282599076 · outbound

This paper cites In- telligent unmanned ground vehicles: autonomous navigation research at Carnegie Mellon.

Behavior Backdoor for Deep Learning Models In- telligent unmanned ground vehicles: autonomous navigation research at Carnegie Mellon

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:22.062553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 92a5af53-2353-43ac-9df5-5ecd527d666f · outbound

This paper cites Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024.

Behavior Backdoor for Deep Learning Models Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:22.041911Z

Source-reported events for the cited work

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Observation 734a6651-69a8-4f18-80bd-f85175879fdb · outbound

This paper cites Quantized neural networks: Training neural networks with low precision weights and ac- tivations.

Behavior Backdoor for Deep Learning Models Quantized neural networks: Training neural networks with low precision weights and ac- tivations

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:22.024115Z

Source-reported events for the cited work

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Observation 0938f234-08d0-4bee-a06a-9fccbfc01063 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

Behavior Backdoor for Deep Learning Models Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:22.000033Z

Source-reported events for the cited work

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Observation 6b3988ff-d0cf-4f78-8177-5d4b0629a5d0 · outbound

This paper cites Backdoor Attacks for In-Context Learning with Language Models.

Behavior Backdoor for Deep Learning Models Backdoor Attacks for In-Context Learning with Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.902384Z

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Observation 3463ac5d-18ab-44b7-8213-aedd5fd17a56 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Behavior Backdoor for Deep Learning Models Adam: A Method for Stochastic Optimization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.908599Z

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Observation f423d43a-15e6-4219-9238-68b33a755b23 · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

Behavior Backdoor for Deep Learning Models Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.914244Z

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Observation f3da22b9-20eb-4423-ac10-bfc608bada98 · outbound

This paper cites Learning multiple layers of features from tiny images.

Behavior Backdoor for Deep Learning Models Learning multiple layers of features from tiny images

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.922973Z

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Observation 0b0b3a03-1b1b-42fa-a279-35daf769b820 · outbound

This paper cites Imagenet classification with deep convolutional neural net- works.

Behavior Backdoor for Deep Learning Models Imagenet classification with deep convolutional neural net- works

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.929545Z

Source-reported events for the cited work

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Observation 05f25963-1c26-47d6-be83-2395322b4030 · outbound

This paper cites Optimal brain damage.

Behavior Backdoor for Deep Learning Models Optimal brain damage

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.947550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8c5a7bdc-a125-4fda-bfe5-b684b067b130 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Behavior Backdoor for Deep Learning Models The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.946081Z

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Observation 9a51847f-6c39-4b14-b18f-c09363565a65 · outbound

This paper cites Ternary Weight Networks.

Behavior Backdoor for Deep Learning Models Ternary Weight Networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.952851Z

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Observation 327440a2-f4b8-4dd5-9902-809164dd9cea · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified 10 vision-language understanding and generation.

Behavior Backdoor for Deep Learning Models Blip: Bootstrapping language-image pre-training for unified 10 vision-language understanding and generation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.923059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5d358208-773c-40dd-8521-19d529fbde74 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Behavior Backdoor for Deep Learning Models Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.967914Z

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Observation b27081f1-2e34-46da-b685-849654580f43 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Behavior Backdoor for Deep Learning Models Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.976190Z

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Observation e228cae1-f08f-4149-9e59-0cbef5d42fcc · outbound

This paper cites Celeb-df: A large-scale challenging dataset for deep- fake forensics.

Behavior Backdoor for Deep Learning Models Celeb-df: A large-scale challenging dataset for deep- fake forensics

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.835271Z

Source-reported events for the cited work

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Observation d4979089-602e-4428-a6c2-3ff3998837d7 · outbound

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

Behavior Backdoor for Deep Learning Models Invisible backdoor attack with sample- specific triggers

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:19.999170Z

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Observation ad675452-3e85-4843-817f-b84f436526c6 · outbound

This paper cites Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection.

Behavior Backdoor for Deep Learning Models Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.005642Z

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Unavailable: canonical work link unavailable.

Observation 3cf15d90-1f22-41a2-a2af-86197eb32f6d · outbound

This paper cites Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift.

Behavior Backdoor for Deep Learning Models Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.013969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e21a0d8-4d2b-4d7d-91b4-8539109e95f2 · outbound

This paper cites Girshick, Kaiming He, and Piotr Doll´ar.

Behavior Backdoor for Deep Learning Models Girshick, Kaiming He, and Piotr Doll´ar

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.707843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dfed0eda-b151-4b48-bcd7-971143a5a032 · outbound

This paper cites FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer.

Behavior Backdoor for Deep Learning Models FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.031345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d8d8a4d-a3ab-4cb7-a586-b6f3b81f3eef · outbound

This paper cites Harnessing percep- tual adversarial patches for crowd counting.

Behavior Backdoor for Deep Learning Models Harnessing percep- tual adversarial patches for crowd counting

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.569854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fbbc3e62-124f-41c6-a4ba-91119cad2107 · outbound

This paper cites Post-training quantization for vision trans- former.

Behavior Backdoor for Deep Learning Models Post-training quantization for vision trans- former

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.552352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3063a3ee-ef22-4889-94ac-0d3e3908f74b · outbound

This paper cites A gentle introduction to deep learning in medical image processing.

Behavior Backdoor for Deep Learning Models A gentle introduction to deep learning in medical image processing

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.532538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f337b78d-e022-413a-94d4-be56b0142e3e · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

Behavior Backdoor for Deep Learning Models Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.055733Z

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Observation 3faa8e01-8090-4d3a-993b-660299b312f9 · outbound

This paper cites Importance estimation for neural net- work pruning.

Behavior Backdoor for Deep Learning Models Importance estimation for neural net- work pruning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.512770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5d4e3638-cbff-4552-8751-5785b9b72140 · outbound

This paper cites A White Paper on Neural Network Quantization.

Behavior Backdoor for Deep Learning Models A White Paper on Neural Network Quantization

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.071971Z

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Observation 44d31f79-a481-4ddd-8ea6-2a5058a6e737 · outbound

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

Behavior Backdoor for Deep Learning Models WaNet -- Imperceptible Warping-based Backdoor Attack

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.081305Z

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Observation ca44d452-b097-4ead-b3b8-9d5424835478 · outbound

This paper cites Input-aware dynamic backdoor attack.

Behavior Backdoor for Deep Learning Models Input-aware dynamic backdoor attack

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.490845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3c7b85d1-f21d-461c-891c-1eec1dc9bc61 · outbound

This paper cites Deep learning for medical image processing: Overview, challenges and the future.

Behavior Backdoor for Deep Learning Models Deep learning for medical image processing: Overview, challenges and the future

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.450519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8426e01e-daaa-4cf1-a98e-e25a321d1fe5 · outbound

This paper cites Girshick, and Jian Sun.

Behavior Backdoor for Deep Learning Models Girshick, and Jian Sun

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.390217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 09e8099d-15b2-4668-a655-2bfcf8387b44 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Behavior Backdoor for Deep Learning Models Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.136063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9849ff4c-a4ed-498e-84e0-23c4a5b11950 · outbound

This paper cites Nipq: Noise proxy- based integrated pseudo-quantization.

Behavior Backdoor for Deep Learning Models Nipq: Noise proxy- based integrated pseudo-quantization

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.142203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91a06741-570e-4282-a149-0dc35588440f · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Behavior Backdoor for Deep Learning Models Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.148819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 251bb0cd-82bb-4db9-b528-090c8b0fe6cb · outbound

This paper cites Going deeper with convolutions.

Behavior Backdoor for Deep Learning Models Going deeper with convolutions

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.155198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 713a4891-32b0-49f4-be69-a1eec6b1920f · outbound

This paper cites Convolutional neural networks for medical im- age analysis: Full training or fine tuning? IEEE transactions on medical imaging, 35(5):1299–1312, 2016.

Behavior Backdoor for Deep Learning Models Convolutional neural networks for medical im- age analysis: Full training or fine tuning? IEEE transactions on medical imaging, 35(5):1299–1312, 2016

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.318933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 638e7685-a8dc-4638-bb5a-ee22325df62f · outbound

This paper cites Towards real-world x-ray security inspection: A high-quality benchmark and lateral inhibition module for prohibited items detection.

Behavior Backdoor for Deep Learning Models Towards real-world x-ray security inspection: A high-quality benchmark and lateral inhibition module for prohibited items detection

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.296216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7bcc4e13-5399-49d9-ba73-7e64d244d575 · outbound

This paper cites Exploring endogenous shift for cross-domain detec- tion: A large-scale benchmark and perturbation suppression network.

Behavior Backdoor for Deep Learning Models Exploring endogenous shift for cross-domain detec- tion: A large-scale benchmark and perturbation suppression network

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.274684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 26a91a11-6c41-4a45-b616-d1f21d1f4562 · outbound

This paper cites Few-shot x-ray prohibited item detection: A benchmark and weak-feature enhancement net- work.

Behavior Backdoor for Deep Learning Models Few-shot x-ray prohibited item detection: A benchmark and weak-feature enhancement net- work

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.254604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7037306a-5f5d-4a5a-8dfc-2b40bb8789b0 · outbound

This paper cites Visualizing data using t-sne.

Behavior Backdoor for Deep Learning Models Visualizing data using t-sne

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.189201Z

Source-reported events for the cited work

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Observation c0a3fe95-11a1-4b9d-83e5-c4aef5566fef · outbound

This paper cites Uni- versal adversarial patch attack for automatic checkout using perceptual and attentional bias.

Behavior Backdoor for Deep Learning Models Uni- versal adversarial patch attack for automatic checkout using perceptual and attentional bias

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.192905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f1cbb13b-0b79-464c-941e-840a6c091a1f · outbound

This paper cites Dual attention suppression attack: Generate adversarial camouflage in physical world.

Behavior Backdoor for Deep Learning Models Dual attention suppression attack: Generate adversarial camouflage in physical world

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.172794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation adc123d8-711e-484c-bf25-89cf5e435eae · outbound

This paper cites De- fensive patches for robust recognition in the physical world.

Behavior Backdoor for Deep Learning Models De- fensive patches for robust recognition in the physical world

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.151910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 34943ca7-7a56-4aa3-abe1-28aea462bcde · outbound

This paper cites Gener- ate transferable adversarial physical camouflages via triplet attention suppression.

Behavior Backdoor for Deep Learning Models Gener- ate transferable adversarial physical camouflages via triplet attention suppression

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.130891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6b27b070-9329-4f5a-ab9a-8899a2ec6b83 · outbound

This paper cites an unresolved cited work.

Behavior Backdoor for Deep Learning Models Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-12T04:29:21.109067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f3025a7a-c546-4970-a22e-eb45ff1d6843 · outbound

This paper cites Grow- ing a brain: Fine-tuning by increasing model capacity.

Behavior Backdoor for Deep Learning Models Grow- ing a brain: Fine-tuning by increasing model capacity

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.086567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation faa0d9bb-fc43-406a-a2e1-5c6b13cfe470 · outbound

This paper cites Convo- lutional neural network pruning with structural redundancy reduction.

Behavior Backdoor for Deep Learning Models Convo- lutional neural network pruning with structural redundancy reduction

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.064874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a2780293-fde4-4d94-8cd0-f63923e34bd0 · outbound

This paper cites Napguard: Towards detecting naturalistic ad- versarial patches.

Behavior Backdoor for Deep Learning Models Napguard: Towards detecting naturalistic ad- versarial patches

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.046508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 38837741-6d50-4d8e-b381-dddf05aa7e06 · outbound

This paper cites A comprehensive overview of backdoor attacks in large language models within communi- cation networks.

Behavior Backdoor for Deep Learning Models A comprehensive overview of backdoor attacks in large language models within communi- cation networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:21.025843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4cda4958-26ba-42c2-9d07-e080cd846e79 · outbound

This paper cites Im- proving deepfake detection generalization by invariant risk minimization.

Behavior Backdoor for Deep Learning Models Im- proving deepfake detection generalization by invariant risk minimization

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:20.999964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bed87e46-91a1-4a0a-b870-0c93d7d32a56 · outbound

This paper cites Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization.

Behavior Backdoor for Deep Learning Models Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.292349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8484327c-079f-46e4-9286-7c165625bec0 · outbound

This paper cites A study on key technologies of unmanned driving.

Behavior Backdoor for Deep Learning Models A study on key technologies of unmanned driving

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:20.963152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1300ed92-ad7a-4a17-b168-d1706d60038b · outbound

This paper cites Diversifying sample generation for accurate data-free quantization.

Behavior Backdoor for Deep Learning Models Diversifying sample generation for accurate data-free quantization

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:20.944265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ddf508ce-46d8-4aa6-9f24-643712747af1 · outbound

This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.

Behavior Backdoor for Deep Learning Models DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-12T04:29:20.316099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bb7ed91-1737-416f-a496-3bf8e1e482a1 · outbound

This paper cites Object detection in 20 years: A survey.Proceed- ings of the IEEE, 111(3):257–276, 2023.

Behavior Backdoor for Deep Learning Models Object detection in 20 years: A survey.Proceed- ings of the IEEE, 111(3):257–276, 2023

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:29:20.913130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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