Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks | Paper Citation Record

Observation 6f59b5e1-a1bc-4132-973b-d6cfeadaee28 · outbound

This paper cites Intriguing properties of neural networks.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Intriguing properties of neural networks

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This paper cites Explaining and Harnessing Adversarial Examples.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Explaining and Harnessing Adversarial Examples

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

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Towards evaluating the robustness of neural networks,

Reference 3

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

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 4

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This paper cites Reliable evaluation of adversarial ro- bustness with an ensemble of diverse parameter-free attacks,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Reliable evaluation of adversarial ro- bustness with an ensemble of diverse parameter-free attacks,

Reference 5

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Observation 92a9f79d-f2ca-4e0f-be20-fd4ead5becfe · outbound

This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,

Reference 6

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Observation 03eb0b7c-3035-462e-819a-98f938dcf976 · outbound

This paper cites Imbalanced gradients: a subtle cause of overestimated adversarial robustness,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Imbalanced gradients: a subtle cause of overestimated adversarial robustness,

Reference 7

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Observation d3e7226e-743c-44f7-a891-39d1abbeec1c · outbound

This paper cites Practical evaluation of adversarial robustness via adaptive auto attack,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Practical evaluation of adversarial robustness via adaptive auto attack,

Reference 8

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Observation d95ddf3b-5c5e-4bce-b4f8-0589fba60d40 · outbound

This paper cites Diversity can be trans- ferred: Output diversification for white-and black-box at- tacks,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Diversity can be trans- ferred: Output diversification for white-and black-box at- tacks,

Reference 9

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Observation 5258fbb7-4090-46b9-983f-53d9780156d2 · outbound

This paper cites An Alternative Surrogate Loss for PGD-based Adversarial Testing.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks An Alternative Surrogate Loss for PGD-based Adversarial Testing

Reference 10

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Observation 6b8b955b-981e-4dba-9aa6-45094dd5f7a1 · outbound

This paper cites Lafeat: Piercing through adversarial defenses with latent features,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Lafeat: Piercing through adversarial defenses with latent features,

Reference 11

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Observation c0ca2ec6-59b3-4379-a4ae-b35935e080a7 · outbound

This paper cites Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks

Reference 12

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Observation ccd0f513-a141-4ad1-b9c6-d5acf4a06dfd · outbound

This paper cites Efficient loss function by minimizing the detrimental effect of floating-point errors on gradient-based attacks,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Efficient loss function by minimizing the detrimental effect of floating-point errors on gradient-based attacks,

Reference 13

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This paper cites Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning,

Reference 14

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This paper cites Adversarial examples in the physical world.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Adversarial examples in the physical world

Reference 15

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Observation 72e81e8a-f7e3-42ae-b84f-226a3c2401a0 · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Theoretically principled trade-off between robustness and accuracy,

Reference 16

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Observation a5575de3-de62-4c61-8767-ad45ce16317c · outbound

This paper cites Minimally distorted adversarial ex- amples with a fast adaptive boundary attack,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Minimally distorted adversarial ex- amples with a fast adaptive boundary attack,

Reference 17

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This paper cites Square attack: a query-efficient black-box adversarial attack via random search,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Square attack: a query-efficient black-box adversarial attack via random search,

Reference 18

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This paper cites Diversified adversarial attacks based on conjugate gradient method,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Diversified adversarial attacks based on conjugate gradient method,

Reference 19

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This paper cites Robust- bench: a standardized adversarial robustness benchmark,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Robust- bench: a standardized adversarial robustness benchmark,

Reference 20

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This paper cites Deep residual learning for image recognition,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Deep residual learning for image recognition,

Reference 21

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This paper cites Wide Residual Networks.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Wide Residual Networks

Reference 22

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

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 23

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This paper cites Xcit: Cross-covariance image transformers,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Xcit: Cross-covariance image transformers,

Reference 24

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 25

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This paper cites A convnet for the 2020s,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks A convnet for the 2020s,

Reference 26

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This paper cites Robust Principles: Architectural Design Principles for Adversarially Robust CNNs.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Robust Principles: Architectural Design Principles for Adversarially Robust CNNs

Reference 27

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This paper cites Better diffusion models further improve adversarial training,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Better diffusion models further improve adversarial training,

Reference 28

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This paper cites Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing

Reference 29

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This paper cites Decoupled Kullback-Leibler Divergence Loss.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Decoupled Kullback-Leibler Divergence Loss

Reference 30

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This paper cites Fixing Data Augmentation to Improve Adversarial Robustness.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Fixing Data Augmentation to Improve Adversarial Robustness

Reference 31

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This paper cites Improving robustness using generated data,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Improving robustness using generated data,

Reference 32

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This paper cites Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

Reference 33

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This paper cites Revisiting residual networks for adversarial robustness,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Revisiting residual networks for adversarial robustness,

Reference 34

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This paper cites A light recipe to train robust vision transformers,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks A light recipe to train robust vision transformers,

Reference 35

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Observation 636a85df-0fbb-47c9-9d5c-9d19c3f2cb96 · outbound

This paper cites Robustness and accuracy could be reconcilable by (proper) definition,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Robustness and accuracy could be reconcilable by (proper) definition,

Reference 36

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This paper cites A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking

Reference 37

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This paper cites MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers

Reference 38

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This paper cites Revisiting adversarial training for imagenet: Architectures, training and general- ization across threat models,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Revisiting adversarial training for imagenet: Architectures, training and general- ization across threat models,

Reference 39

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This paper cites Local intrinsic dimensionality I: an extreme- value-theoretic foundation for similarity applications,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Local intrinsic dimensionality I: an extreme- value-theoretic foundation for similarity applications,

Reference 40

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This paper cites Char- acterizing adversarial subspaces using local intrinsic dimen- sionality,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Char- acterizing adversarial subspaces using local intrinsic dimen- sionality,

Reference 41

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This paper cites Collider: A robust training framework for backdoor data,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Collider: A robust training framework for backdoor data,

Reference 42

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This paper cites Detecting backdoor samples in contrastive language image pretraining,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Detecting backdoor samples in contrastive language image pretraining,

Reference 43

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This paper cites Ldreg: Local dimensionality regular- ized self-supervised learning,.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Ldreg: Local dimensionality regular- ized self-supervised learning,

Reference 44

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This paper cites an unresolved cited work.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Unresolved cited work

Reference 45

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This paper cites Figure 2.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Figure 2

Reference 46

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This paper cites The models’ robustness (%) evaluated on different K ′ values.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks The models’ robustness (%) evaluated on different K ′ values

Reference 47

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Observation ccfc8d40-5e3c-4ab1-b4a6-8a3b4b73a849 · outbound

This paper cites Seven defense models from the CIFAR10 dataset were subjected to a con- straint of 100 attack steps.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Seven defense models from the CIFAR10 dataset were subjected to a con- straint of 100 attack steps

Reference 48

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This paper cites To extend our analysis, this section introduces comparative experiments with optimizer-based approaches, focusing on the widely recognized Adam optimizer.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks To extend our analysis, this section introduces comparative experiments with optimizer-based approaches, focusing on the widely recognized Adam optimizer

Reference 49

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Observation 41b4df7c-b369-4913-9ba2-ebc8dea89eb2 · outbound

This paper cites The robustness (%) of the models, evaluated using the PGDpm attack with varying β values, on the CIFAR10 and Ima- geNet datasets.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks The robustness (%) of the models, evaluated using the PGDpm attack with varying β values, on the CIFAR10 and Ima- geNet datasets

Reference 50

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Observation b34c366c-5182-4bcc-8699-3afcec9acc30 · outbound

This paper cites We assessed the same set of five ImageNet defense models discussed in the main body of the paper.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks We assessed the same set of five ImageNet defense models discussed in the main body of the paper

Reference 51

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This paper cites We tested perturbation ranges of 1, 2, and 3, with a batch size of 32.

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks We tested perturbation ranges of 1, 2, and 3, with a batch size of 32

Reference 52

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T16:50:39.656094Z

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