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v0.3

  • New Attacks : FGSM, IFGSM, IterLL, RFGSM, CW(L2), PGD are added.
  • Demos are uploaded.

v0.4

  • DO NOT USE : 'init.py' is omitted.

v0.5

  • Package name changed : 'attacks' is changed to 'torchattacks'.
  • New Attack : APGD is added.
  • attack.py : 'update_model' method is added.

v0.6

  • Error Solved :
    • Before this version, even after getting an adversarial image, the model remains evaluation mode.
    • To solve this, below methods are modified.
      • '_switch_model' method is added into attack.py. It will automatically change model mode to the previous mode after getting adversarial images. When getting adversarial images, model is switched to evaluation mode.
      • 'call' methods in all attack changed to forward. Instead of this, 'call' method is added into 'attack.py'
  • attack.py : To provide ease of changing images to uint8 from float, 'set_mode' and '_to_uint' is added.
    • 'set_mode' determines returning all outputs as 'int' OR 'flaot' through '_to_uint'.
    • '_to_uint' changes all outputs into uint8.

v0.7

  • All attacks are modified
    • clone().detach() is used instead of .data
    • torch.autograd.grad is used instead of .backward() and .grad :
      • It showed 2% reduction of computation time.

v0.8

  • New Attack : RPGD is added.
  • attack.py : 'update_model' method is depreciated. Because torch models are passed by call-by-reference, we don't need to update models.
    • cw.py : In the process of cw attack, now masked_select uses a mask with dtype torch.bool instead of a mask with dtype torch.uint8.

v0.9

  • New Attack : DeepFool is added.
  • Some attacks are renamed :
    • I-FGSM -> BIM
    • IterLL -> StepLL

v1.0

  • attack.py :
    • load : Load is depreciated. Instead, use TensorDataset and DataLoader.
    • save : The problem of calculating invalid accuracy when the mode of the attack set to 'int' is solved.

v1.1

v1.2

  • Description has been added for each module.
  • Sphinx Document uploaded
  • attack.py : 'device' will be decided by next(model.parameters()).device.
  • Two attacks are merged :
    • RPGD, PGD -> PGD

v1.3

  • Pip Package Re-uploaded.

v1.4

  • PGD:
    • Now PGD supports targeted mode.

v1.5

  • MultiAttack:
    • MultiAttack is added.
    • With it, you can use PGD with N-random-restarts or stronger attacks with different methods.

v2.4

  • steps instead of iters:
    • For compatibility reasons, all iters are changed to steps.
  • TPGD:
  • FFGSM:

v2.5

  • Methods for Attack are added:
    • set_attack_mode: To set attack mode to targeted (Use input labels as targeted labels) or least likely (Use least likely labels as targeted labels), set_attack_mode is added.
      • StepLL is merged to BIM. Please use set_attack_mode(mode='least_likely').
      • However, there are several methods that can not be changed by set_attack_mode such as Deepfool
    • set_return_type: Instead of set_mode, now set_return_type will be the method to change the return type of adversarial images.

v2.6

v2.9

  • VANILA:
    • Vanila version of torch.Attack.
  • MultiAttack:
    • MultiAttack does not need a model as an input. It automatically get models from given attacks.
    • Demo added.
  • Attack.set_attack_mode:
    • For the targeted mode, target_map_function is required.

v2.10

  • GN:
    • Add guassian noise with given sigma.

v2.10.3

  • TPGD: Faster computation

v2.10.4

  • attacks : To preserve the original gradient status of images, all attacks uses images.clone().detach() instead of images.

v2.11.0

  • CW
    • Now it outputs the best L2 adversarial images.
    • Faster computation.
  • DeepFool
    • Make the codes cleaner.
  • BIM
    • Bug fixed: Wrong cliping.
  • MIFGSM
  • Demo Added
    • Performance Comparison (CIFAR10)

v2.12.1

  • DeepFool
    • Deprecated.
  • Attack._targeted
    • ._targeted is set to 1 when targeted mode is activated. Issue.
      • All attacks supporting targeted mode is change.
  • Attack.set_attack_mode
    • To provide various attack mode, it uses following methods.
      • set_default_mode: default mode.
      • set_targeted_mode: targeted mode. Now supporting target_map_function=None for pre-generated targeted labels.
      • set_least_likely_mode: least likely targeted mode. Now supporting k-th smallest probability targeted mode by kth_min.
  • Attack.save
    • Bug fixed: When verbose=True, it now use model.eval() and torch.no_grad().

v2.12.1

  • DeepFool
    • Deprecated.
  • Attack._targeted
    • ._targeted is set to 1 when targeted mode is activated. Issue.
      • All attacks supporting targeted mode is change.
  • Attack.set_attack_mode
    • To provide various attack mode, it uses following methods.
      • set_default_mode: default mode.
      • set_targeted_mode: targeted mode. Now supporting target_map_function=None for pre-generated targeted labels.
      • set_least_likely_mode: least likely targeted mode. Now supporting k-th smallest probability targeted mode by kth_min.
  • Attack.save
    • Bug fixed: When verbose=True, it now use model.eval() and torch.no_grad().

v2.12.2

  • PGDL2
    • PGD with L2 distance measure.
  • Attack.save
    • Print L2 distance between adversarial examples and the original examples.

v2.12.3

  • PGDL2

    • Initialization perturbation is changed.

v2.13.1

  • Attack.set_attack_mode
    • Deprecated. Use following built-in functions.
      • set_mode_default: default mode.
      • set_mode_targeted: targeted mode. Now supporting target_map_function=None for pre-generated targeted labels.
      • set_mode_least_likely: least likely targeted mode. Now supporting k-th smallest probability targeted mode by kth_min.
  • APGD is changed to EOTPGD.
  • PGDDLR is added.
  • APGD, APGDT, Square, FAB
    • Modified from https://github.com/fra31/auto-attack.
      • n_iters is changed to steps.
      • n_target_classes is calculated based on n_claases.
      • reduce=False is erased because it is enough with reduction='none'.
  • AutoAttack
    • Created based on APGD, APGDT, Square, FAB.

v2.13.2

  • Attack.save
    • Don't use an additional memory if save_path=None

v2.14.0

  • DeepFool, OnePixel, SparseFool are added.

v2.14.1

  • Attack.set_training_mode
    • The method to support changing the model to training mode.
    • Note that RNN requires model.training=True to calculate gradient.

v2.14.2

  • SparseFool

v2.14.3

  • DI2FGSM is added.

v2.14.4

  • Square is fixed.
    • If idx_to_fool is empty, then terminate an iteration.

v2.14.5

v3.0.0

  • torch=1.9.0 supported.

  • Targeted mode is officially supported.

    • Attack & Attacks.*

      • set_mode_default

      • set_mode_targeted_by_function

      • set_mode_targeted_least_likely

      • set_mode_targeted_random

      • _get_target_label

      • _get_least_likely_label

      • _get_random_target_label

      • self._supported_mode

      • self._targeted

  • UPGD created.

    • Utimate PGD that supports various options of gradient-based adversarial attacks.

  • DIFGSM is fixed.

  • Extra

    • Iteration variable (e.g., for i in range) is replaced to _ if it is not needed.
    • MultiAttack now prints the attack success rate for each attack.
    • Arguments for super() is erased.

v3.1.0

v3.2.0

  • Jitter is added.

  • Attack.*

    • set_training_mode: Now supports changing training mode of Batchnorm and Dropout.
    • save: Now supports return values of the last verbose information.

  • MultiAttack

    • Fixed the verbose function.
    • Now supports return values of the last verbose information

v3.2.1

  • GN: sigma is changed to std.

v3.2.2

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v3.2.3

  • save, MultiAttack: Now supports saving predictions.

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v3.2.4

  • save, MultiAttack: return_verbose can be True even if verbose=False.

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v3.2.5

  • Pixle is added.
  • save: Now saving images and labels for every batch.
  • OnePixel: Now supports targeted version.
  • _get_target_label: Now generates target label under evaluation mode and torch.no_grad().

v3.2.6