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Torchattacks: A PyTorch Repository for Adversarial Attacks

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arxiv 2010.01950 v3 pith:BWTCW26M submitted 2020-09-24 cs.LG cs.AIcs.CR

Torchattacks: A PyTorch Repository for Adversarial Attacks

Hoki Kim This is my paper
classification cs.LG cs.AIcs.CR
keywords adversarialattackspytorchtorchattacksadversarial-attacks-pytorchcodecontainsdeep
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Torchattacks is a PyTorch library that contains adversarial attacks to generate adversarial examples and to verify the robustness of deep learning models. The code can be found at https://github.com/Harry24k/adversarial-attacks-pytorch.

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Cited by 14 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP

    cs.CV 2026-06 unverdicted novelty 7.0

    A^4D detects adversarial attacks in an attack- and classifier-agnostic way by measuring non-arbitrary shifts in CLIP embedding space from prompt-based similarity scores.

  2. A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP

    cs.CV 2026-06 unverdicted novelty 7.0

    A^4D is a classifier- and attack-agnostic zero-shot adversarial attack detector based on CLIP embedding shifts that claims SOTA performance.

  3. Low Rank Adaptation for Adversarial Perturbation

    cs.LG 2026-04 unverdicted novelty 7.0

    Adversarial perturbations possess an inherently low-rank structure that enables more efficient and effective black-box adversarial attacks via subspace projection.

  4. Towards Generalized Certified Robustness with Multi-Norm Training

    cs.LG 2024-10 unverdicted novelty 7.0

    CURE is the first multi-norm certified training method that improves union robustness across l_p norms and unseen perturbations on MNIST, CIFAR-10 and TinyImagenet.

  5. Two Sides of the Same Coin: Learning the Backdoor to Remove the Backdoor

    cs.LG 2026-07 conditional novelty 6.5

    Learning a backdoored reference model as a poisonous-sample oracle enables near-perfect training-time backdoor removal with negligible natural-accuracy loss.

  6. Test Case Prioritization for DNNs via Neural Collapse Instability

    cs.LG 2026-07 conditional novelty 6.0

    DNN test inputs ranked by prediction instability across late-training checkpoints find faults earlier than confidence-based ranking in most benchmark settings.

  7. TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models

    cs.CV 2026-05 unverdicted novelty 6.0

    TAME uses a Mixture-of-Experts prompt bank with input-dependent routing and three unsupervised objectives to adaptively defend CLIP against adversarial attacks at inference time, achieving at least 49.1% robustness ga...

  8. Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers

    cs.CV 2026-07 conditional novelty 5.0

    FDT adds foveation and binary fixation modules to DeiT so multi-scale tokens are selected dynamically in one pass, improving ImageNet100 accuracy, MACs, and robustness without adversarial training.

  9. A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs

    cs.LG 2026-06 unverdicted novelty 5.0

    A preprocessor of Gaussian noise plus bilateral filtering yields supralinear adversarial robustness in CNNs and, when paired with adversarial training, ranks near the top of RobustBench while using far less compute, p...

  10. QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits

    cs.CR 2026-04 unverdicted novelty 5.0

    Hybrid quantum-classical models using structured entanglement keep high accuracy on MNIST, OrganAMNIST and CIFAR-10 while lowering adversarial attack success rates and raising the computational cost of generating attacks.

  11. QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits

    cs.CR 2026-04 conditional novelty 5.0

    A CNN-plus-quantum-circuit classifier with learned fusion reports lower attack success rates and much higher attack-generation cost than a CNN baseline on MNIST, OrganAMNIST, and CIFAR-10.

  12. Learning Aligned Stability in Neural ODEs Reconciling Accuracy with Robustness

    cs.LG 2025-09 unverdicted novelty 5.0

    Zubov-Net aligns prescribed regions of attraction defined by learnable Lyapunov functions with true regions in Neural ODEs via a differentiable Zubov consistency loss, claiming to reconcile accuracy and certified robustness.

  13. FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models

    cs.CV 2025-09 conditional novelty 5.0

    FedAPT improves adversarial robustness of federated prompt tuning for CLIP by generating visual prompts from text prompts under a global-label beacon, with reported gains of up to 11.49% under PGD-100.

  14. LLM-Safety Evaluations Lack Robustness

    cs.CR 2025-03 unverdicted novelty 4.0

    LLM safety evaluations are hindered by noise in dataset curation, automated red-teaming, response generation, and LLM-judge evaluation, making fair comparisons difficult and slowing progress.