Torchattacks is a PyTorch library that provides adversarial attacks to generate adversarial examples. It contains PyTorch-like interface and functions that make it easier for PyTorch users to implement adversarial attacks. import torchattacks atk = torchattacks. PGD (model, eps=8/255, alpha=2/255, steps=4)

Adversarial patch attacks mislead neural networks by injecting adversarial pixels within a local region. Patch attacks can be highly effective in a variety of tasks and physically realizable via attachment (e.g. a sticker) to the real-world objects.

According to the existing attack methods on computer vision, the imperceptible perturbations embedded into the initial images can be categorized into noise disturbance and patch disturbance. In this paper, we proposed an attack method that …

patch_external – External patch to apply to images x. Returns: The patched instances. generate (x: ndarray, y: ndarray | None = None, ** kwargs) → Tuple [ndarray, ndarray] ¶ Generate an adversarial patch and return the patch and its mask in arrays. Parameters:

Generate adversarial patches against YOLOv5 🚀 . Contribute to SamSamhuns/yolov5_adversarial development by creating an account on GitHub.

Ultimate PGD that supports various options of gradient-based adversarial attacks. Distance Measure : Linf. Parameters: model (nn.Module) – model to attack. eps (float) – maximum perturbation. (Default: 8/255) alpha (float) – step size. (Default: 2/255) steps (int) – number of steps. (Default: 10)

Examples:: >>> attack = torchattacks.PGD(model, eps=8/255, alpha=1/255, steps=10, random_start=True) >>> adv_images = attack(images, labels) """ def __init__(self, model, eps=8 / 255, alpha=2 / 255, steps=10, random_start=True): super().__init__("PGD", model) self.eps = eps self.alpha = alpha self.steps = steps self.random_st...

A strong adversarial patch consists of two critical at-tributes: the patch position and the patch pattern. The patch pattern can be computed by Projected Gradient Descent (PGD)(Madry et al.,2017), To maximize the loss of the model and alter the output, PGD repeats the gradient descent by multiple times to train the adversarial patch. We denote

Adversarial patch attacks are a form of adversarial attack that use localized, confined patterns of noise (a "patch") to manipulate neural network predictions. Unlike global adversarial examples that require pixel-level access to the entire image, patches can be:

We aim to have the image of a race car misclassified as a tiger, using the ℓ 2-norm targeted implementations of the Carlini-Wagner (CW) attack (from CleverHans), and of our PGD attack. We also...