CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks

• URL: http://arxiv.org/abs/2302.02213v3
• Date: Fri, 5 Jul 2024 18:38:22 GMT
• Title: CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
• Authors: Shashank Agnihotri, Steffen Jung, Margret Keuper,
• Abstract summary: Adversarial attacks such as the seminal projected gradient descent (PGD) offer an effective means to evaluate a model's robustness. We propose CosPGD, an attack that encourages more balanced errors over the entire image domain while increasing the attack's overall efficiency.
• Score: 16.10247754923311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While neural networks allow highly accurate predictions in many tasks, their lack of robustness towards even slight input perturbations often hampers their deployment. Adversarial attacks such as the seminal projected gradient descent (PGD) offer an effective means to evaluate a model's robustness and dedicated solutions have been proposed for attacks on semantic segmentation or optical flow estimation. While they attempt to increase the attack's efficiency, a further objective is to balance its effect, so that it acts on the entire image domain instead of isolated point-wise predictions. This often comes at the cost of optimization stability and thus efficiency. Here, we propose CosPGD, an attack that encourages more balanced errors over the entire image domain while increasing the attack's overall efficiency. To this end, CosPGD leverages a simple alignment score computed from any pixel-wise prediction and its target to scale the loss in a smooth and fully differentiable way. It leads to efficient evaluations of a model's robustness for semantic segmentation as well as regression models (such as optical flow, disparity estimation, or image restoration), and it allows it to outperform the previous SotA attack on semantic segmentation. We provide code for the CosPGD algorithm and example usage at https://github.com/shashankskagnihotri/cospgd.

Related papers

• GreedyPixel: Fine-Grained Black-Box Adversarial Attack Via Greedy Algorithm [21.044241546344846]
  A critical requirement for deep learning models is ensuring their robustness against adversarial attacks. We introduce GreedyPixel, a pixel-wise greedy algorithm designed to generate high-quality adversarial examples. Our results demonstrate that GreedyPixel achieves attack success rates comparable to white-box methods without the need for gradient information.
  arXiv  Detail & Related papers  (2025-01-24T04:17:03Z)
• Rethinking PGD Attack: Is Sign Function Necessary? [131.6894310945647]
  We present a theoretical analysis of how such sign-based update algorithm influences step-wise attack performance. We propose a new raw gradient descent (RGD) algorithm that eliminates the use of sign. The effectiveness of the proposed RGD algorithm has been demonstrated extensively in experiments.
  arXiv  Detail & Related papers  (2023-12-03T02:26:58Z)
• Everything Perturbed All at Once: Enabling Differentiable Graph Attacks [61.61327182050706]
  Graph neural networks (GNNs) have been shown to be vulnerable to adversarial attacks. We propose a novel attack method called Differentiable Graph Attack (DGA) to efficiently generate effective attacks. Compared to the state-of-the-art, DGA achieves nearly equivalent attack performance with 6 times less training time and 11 times smaller GPU memory footprint.
  arXiv  Detail & Related papers  (2023-08-29T20:14:42Z)
• Wasserstein distributional robustness of neural networks [9.79503506460041]
  Deep neural networks are known to be vulnerable to adversarial attacks (AA) For an image recognition task, this means that a small perturbation of the original can result in the image being misclassified. We re-cast the problem using techniques of Wasserstein distributionally robust optimization (DRO) and obtain novel contributions.
  arXiv  Detail & Related papers  (2023-06-16T13:41:24Z)
• Dynamic Transformers Provide a False Sense of Efficiency [75.39702559746533]
  Multi-exit models make a trade-off between efficiency and accuracy, where the saving of computation comes from an early exit. We propose a simple yet effective attacking framework, SAME, which is specially tailored to reduce the efficiency of the multi-exit models. Experiments on the GLUE benchmark show that SAME can effectively diminish the efficiency gain of various multi-exit models by 80% on average.
  arXiv  Detail & Related papers  (2023-05-20T16:41:48Z)
• Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias [50.628150015907565]
  Cross-entropy loss function is used to evaluate perturbation schemes in classification tasks. Previous methods use negative cross-entropy loss as the attack objective in attacking node-level classification models. This paper argues about the previous unreasonable attack objective from the perspective of budget allocation.
  arXiv  Detail & Related papers  (2023-03-29T13:02:02Z)
• Class-Conditioned Transformation for Enhanced Robust Image Classification [19.738635819545554]
  We propose a novel test-time threat model algorithm that enhances Adrial-versa-Trained (AT) models. Our method operates through COnditional image transformation and DIstance-based Prediction (CODIP) The proposed method achieves state-of-the-art results demonstrated through extensive experiments on various models, AT methods, datasets, and attack types.
  arXiv  Detail & Related papers  (2023-03-27T17:28:20Z)
• Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation [26.16745376395128]
  Boundary based blackbox attack has been recognized as practical and effective, given that an attacker only needs to access the final model prediction. We show that such efficiency highly depends on the scale at which the attack is applied, and attacking at the optimal scale significantly improves the efficiency. We propose Progressive-Scale enabled projective Boundary Attack (PSBA) to improve the query efficiency via progressive scaling techniques.
  arXiv  Detail & Related papers  (2021-06-10T21:13:41Z)
• Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm [93.80082636284922]
  Sparse adversarial attacks can fool deep networks (DNNs) by only perturbing a few pixels. Recent efforts combine it with another l_infty perturbation on magnitudes. We propose a homotopy algorithm to tackle the sparsity and neural perturbation framework.
  arXiv  Detail & Related papers  (2021-06-10T20:11:36Z)
• Patch-wise++ Perturbation for Adversarial Targeted Attacks [132.58673733817838]
  We propose a patch-wise iterative method (PIM) aimed at crafting adversarial examples with high transferability. Specifically, we introduce an amplification factor to the step size in each iteration, and one pixel's overall gradient overflowing the $epsilon$-constraint is properly assigned to its surrounding regions. Compared with the current state-of-the-art attack methods, we significantly improve the success rate by 35.9% for defense models and 32.7% for normally trained models.
  arXiv  Detail & Related papers  (2020-12-31T08:40:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.