Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation

• URL: http://arxiv.org/abs/2308.06015v1
• Date: Fri, 11 Aug 2023 08:44:58 GMT
• Title: Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation
• Authors: Xuannan Liu, Yaoyao Zhong, Yuhang Zhang, Lixiong Qin, Weihong Deng
• Abstract summary: Deep neural networks are vulnerable to universal adversarial perturbation (UAP) In this paper, we examine the serious dilemma of UAP generation methods from a generalization perspective. We propose a simple and effective method called Gradient Aggregation (SGA) SGA alleviates the gradient vanishing and escapes from poor local optima at the same time.
• Score: 40.18851174642427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks are vulnerable to universal adversarial perturbation (UAP), an instance-agnostic perturbation capable of fooling the target model for most samples. Compared to instance-specific adversarial examples, UAP is more challenging as it needs to generalize across various samples and models. In this paper, we examine the serious dilemma of UAP generation methods from a generalization perspective -- the gradient vanishing problem using small-batch stochastic gradient optimization and the local optima problem using large-batch optimization. To address these problems, we propose a simple and effective method called Stochastic Gradient Aggregation (SGA), which alleviates the gradient vanishing and escapes from poor local optima at the same time. Specifically, SGA employs the small-batch training to perform multiple iterations of inner pre-search. Then, all the inner gradients are aggregated as a one-step gradient estimation to enhance the gradient stability and reduce quantization errors. Extensive experiments on the standard ImageNet dataset demonstrate that our method significantly enhances the generalization ability of UAP and outperforms other state-of-the-art methods. The code is available at https://github.com/liuxuannan/Stochastic-Gradient-Aggregation.

Related papers

• AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods [17.043034606088234]
  We introduce AdAdaGrad's scalar variant AdAdaGradNorm, which increase sizes during training. We also perform image classification experiments, highlighting the merits of our proposed strategies.
  arXiv  Detail & Related papers  (2024-02-17T07:49:50Z)
• Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms [88.74308282658133]
  Reization (RP) Policy Gradient Methods (PGMs) have been widely adopted for continuous control tasks in robotics and computer graphics. Recent studies have revealed that, when applied to long-term reinforcement learning problems, model-based RP PGMs may experience chaotic and non-smooth optimization landscapes. We propose a spectral normalization method to mitigate the exploding variance issue caused by long model unrolls.
  arXiv  Detail & Related papers  (2023-10-30T18:43:21Z)
• Boosting Adversarial Transferability by Achieving Flat Local Maxima [23.91315978193527]
  Recently, various adversarial attacks have emerged to boost adversarial transferability from different perspectives. In this work, we assume and empirically validate that adversarial examples at a flat local region tend to have good transferability. We propose an approximation optimization method to simplify the gradient update of the objective function.
  arXiv  Detail & Related papers  (2023-06-08T14:21:02Z)
• Scaling Forward Gradient With Local Losses [117.22685584919756]
  Forward learning is a biologically plausible alternative to backprop for learning deep neural networks. We show that it is possible to substantially reduce the variance of the forward gradient by applying perturbations to activations rather than weights. Our approach matches backprop on MNIST and CIFAR-10 and significantly outperforms previously proposed backprop-free algorithms on ImageNet.
  arXiv  Detail & Related papers  (2022-10-07T03:52:27Z)
• Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization [61.26619639722804]
  We propose a conditional gradient method (CGM) for minimizing convex finite-sum objectives formed as a sum of smooth and non-smooth terms. The proposed method, equipped with an average gradient (SAG) estimator, requires only one sample per iteration. Nevertheless, it guarantees fast convergence rates on par with more sophisticated variance reduction techniques.
  arXiv  Detail & Related papers  (2022-02-26T19:10:48Z)
• Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework [100.36569795440889]
  This work is on the iteration of zero-th-order (ZO) optimization which does not require first-order information. We show that with a graceful design in coordinate importance sampling, the proposed ZO optimization method is efficient both in terms of complexity as well as as function query cost.
  arXiv  Detail & Related papers  (2020-12-21T17:29:58Z)
• On the Convergence of SGD with Biased Gradients [28.400751656818215]
  We analyze the guiding domain of biased gradient methods (SGD), where individual updates are corrupted by compression. We quantify how many magnitudes of bias accuracy and convergence rates are impacted.
  arXiv  Detail & Related papers  (2020-07-31T19:37:59Z)
• Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning [24.12941820827126]
  We propose a biased gradient descent (BSGD) for Conditional optimization problems. Our lower bound analysis shows that BSGD cannot be improved for general convex objectives non objectives. For this special setting, we propose an accelerated algorithm called biased SpiderBoost (BSpiderBoost) that matches the lower bound.
  arXiv  Detail & Related papers  (2020-02-25T10:57:38Z)
• Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets [71.05306664267832]
  Adaptive algorithms perform gradient updates using the history of gradients and are ubiquitous in training deep neural networks. In this paper we analyze a variant of OptimisticOA algorithm for nonconcave minmax problems. Our experiments show that adaptive GAN non-adaptive gradient algorithms can be observed empirically.
  arXiv  Detail & Related papers  (2019-12-26T22:10:10Z)

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.