secmlt.trackers package

Submodules

secmlt.trackers.image_trackers module

Image-specific trackers.

class secmlt.trackers.image_trackers.ImageGradientsTracker

Bases: GradientsTracker

Tracker for gradient images.

class secmlt.trackers.image_trackers.ImageSampleTracker

Bases: SampleTracker

Tracker for adversarial examples.

secmlt.trackers.tensorboard_tracker module

Tensorboard tracking utilities.

class secmlt.trackers.tensorboard_tracker.TensorboardTracker(logdir: str, trackers: list[Tracker] | None = None)

Bases: Tracker

Tracker for Tensorboard. Uses other trackers as subscribers.

end_tracking() → None

End tracking for current batch and update global sample offset.

get_last_tracked() → NotImplementedError

Not implemented for this tracker.

init_tracking() → None

Initialize tracking for a new batch.

track(iteration: int, loss: torch.Tensor, scores: torch.Tensor, x_adv: torch.tensor, delta: torch.Tensor, grad: torch.Tensor) → None

Update all subscribed trackers.

Parameters:

• iteration (int) – The attack iteration number.

• loss (torch.Tensor) – The value of the (per-sample) loss of the attack.

• scores (torch.Tensor) – The output scores from the model.

• x_adv (torch.tensor) – The adversarial examples at the current iteration.

• delta (torch.Tensor) – The adversarial perturbations at the current iteration.

• grad (torch.Tensor) – The gradient of delta at the given iteration.

secmlt.trackers.trackers module

Trackers for attack metrics.

class secmlt.trackers.trackers.GradientNormTracker(p: LpPerturbationModels = 'l2')

Bases: Tracker

Tracker for gradients.

track(iteration: int, loss: Tensor, scores: Tensor, x_adv: tensor, delta: Tensor, grad: Tensor) → None

Track the sample-wise gradient of the loss w.r.t delta.

Parameters:

• iteration (int) – The attack iteration number.

• loss (torch.Tensor) – The value of the (per-sample) loss of the attack.

• scores (torch.Tensor) – The output scores from the model.

• x_adv (torch.tensor) – The adversarial examples at the current iteration.

• delta (torch.Tensor) – The adversarial perturbations at the current iteration.

• grad (torch.Tensor) – The gradient of delta at the given iteration.

class secmlt.trackers.trackers.GradientsTracker(tracker_type: str = 'multiple_scalars')

Bases: Tracker

Generic tracker for gradients.

track(iteration: int, loss: Tensor, scores: Tensor, x_adv: Tensor, delta: Tensor, grad: Tensor) → None

Track the gradients at the current iteration.

Parameters:

• iteration (int) – The attack iteration number.

• loss (torch.Tensor) – The value of the (per-sample) loss of the attack.

• scores (torch.Tensor) – The output scores from the model.

• x_adv (torch.Tensor) – The adversarial examples at the current iteration.

• delta (torch.Tensor) – The adversarial perturbations at the current iteration.

• grad (torch.Tensor) – The gradient of delta at the given iteration.

class secmlt.trackers.trackers.LossTracker

Bases: Tracker

Tracker for attack loss.

track(iteration: int, loss: Tensor, scores: Tensor, x_adv: tensor, delta: Tensor, grad: Tensor) → None

Track the sample-wise loss of the attack at the current iteration.

Parameters:

• iteration (int) – The attack iteration number.

• loss (torch.Tensor) – The value of the (per-sample) loss of the attack.

• scores (torch.Tensor) – The output scores from the model.

• x_adv (torch.tensor) – The adversarial examples at the current iteration.

• delta (torch.Tensor) – The adversarial perturbations at the current iteration.

• grad (torch.Tensor) – The gradient of delta at the given iteration.

class secmlt.trackers.trackers.PerturbationNormTracker(p: LpPerturbationModels = 'l2')

Bases: Tracker

Tracker for perturbation norm.

track(iteration: int, loss: Tensor, scores: Tensor, x_adv: tensor, delta: Tensor, grad: Tensor) → None

Track the perturbation norm at the current iteration.

Parameters:

• iteration (int) – The attack iteration number.

• loss (torch.Tensor) – The value of the (per-sample) loss of the attack.

• scores (torch.Tensor) – The output scores from the model.

• x_adv (torch.tensor) – The adversarial examples at the current iteration.

• delta (torch.Tensor) – The adversarial perturbations at the current iteration.

• grad (torch.Tensor) – The gradient of delta at the given iteration.

class secmlt.trackers.trackers.PredictionTracker

Bases: Tracker

Tracker for model predictions.

track(iteration: int, loss: Tensor, scores: Tensor, x_adv: tensor, delta: Tensor, grad: Tensor) → None

Track the sample-wise model predictions at the current iteration.

Parameters:

• iteration (int) – The attack iteration number.

• loss (torch.Tensor) – The value of the (per-sample) loss of the attack.

• scores (torch.Tensor) – The output scores from the model.

• x_adv (torch.tensor) – The adversarial examples at the current iteration.

• delta (torch.Tensor) – The adversarial perturbations at the current iteration.

• grad (torch.Tensor) – The gradient of delta at the given iteration.

class secmlt.trackers.trackers.SampleTracker(tracker_type: str = 'multiple_scalars')

Bases: Tracker

Generic tracker for adversarial samples.

track(iteration: int, loss: Tensor, scores: Tensor, x_adv: Tensor, delta: Tensor, grad: Tensor) → None

Track adversarial examples at the current iteration.

Parameters:

• iteration (int) – The attack iteration number.

• loss (torch.Tensor) – The value of the (per-sample) loss of the attack.

• scores (torch.Tensor) – The output scores from the model.

• x_adv (torch.Tensor) – The adversarial examples at the current iteration.

• delta (torch.Tensor) – The adversarial perturbations at the current iteration.

• grad (torch.Tensor) – The gradient of delta at the given iteration.

class secmlt.trackers.trackers.ScoresTracker(y: int | Tensor | None = None)

Bases: Tracker

Tracker for model scores.

track(iteration: int, loss: Tensor, scores: Tensor, x_adv: tensor, delta: Tensor, grad: Tensor) → None

Track the sample-wise model scores at the current iteration.

Parameters:

• iteration (int) – The attack iteration number.

• loss (torch.Tensor) – The value of the (per-sample) loss of the attack.

• scores (torch.Tensor) – The output scores from the model.

• x_adv (torch.tensor) – The adversarial examples at the current iteration.

• delta (torch.Tensor) – The adversarial perturbations at the current iteration.

• grad (torch.Tensor) – The gradient of delta at the given iteration.

class secmlt.trackers.trackers.Tracker(name: str, tracker_type: str = 'scalar')

Bases: ABC

Class implementing the trackers for the attacks.

end_tracking() → None

Finalize the current batch and append its history to _batches.

get() → Tensor

Get the current tracking history.

Returns:

History of tracked parameters. When multiple batches were tracked, returns a tensor where batches are concatenated along the sample dimension (dim=0) and iterations are along the last dimension.

Return type:

torch.Tensor

get_last_tracked() → None | Tensor

Get last element tracked.

Returns:

Returns the last tracked element if anything was tracked.

Return type:

None | torch.Tensor

init_tracking() → None

Initialize tracking for a new batch (clears the per-batch buffer).

reset() → None

Clear all tracking history across all batches.

abstract track(iteration: int, loss: Tensor, scores: Tensor, x_adv: tensor, delta: Tensor, grad: Tensor) → None

Track the history of given attack observable parameters.

Parameters:

• iteration (int) – The attack iteration number.

• loss (torch.Tensor) – The value of the (per-sample) loss of the attack.

• scores (torch.Tensor) – The output scores from the model.

• x_adv (torch.tensor) – The adversarial examples at the current iteration.

• delta (torch.Tensor) – The adversarial perturbations at the current iteration.

• grad (torch.Tensor) – The gradient of delta at the given iteration.

Module contents

Module implementing trackers for adversarial attacks.