An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability

• URL: http://arxiv.org/abs/2308.02897v1
• Date: Sat, 5 Aug 2023 15:12:36 GMT
• Title: An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability
• Authors: Bin Chen, Jia-Li Yin, Shukai Chen, Bo-Hao Chen and Ximeng Liu
• Abstract summary: We propose an adaptive ensemble attack, dubbed AdaEA, to adaptively control the fusion of the outputs from each model. We achieve considerable improvement over the existing ensemble attacks on various datasets.
• Score: 26.39964737311377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While the transferability property of adversarial examples allows the adversary to perform black-box attacks (i.e., the attacker has no knowledge about the target model), the transfer-based adversarial attacks have gained great attention. Previous works mostly study gradient variation or image transformations to amplify the distortion on critical parts of inputs. These methods can work on transferring across models with limited differences, i.e., from CNNs to CNNs, but always fail in transferring across models with wide differences, such as from CNNs to ViTs. Alternatively, model ensemble adversarial attacks are proposed to fuse outputs from surrogate models with diverse architectures to get an ensemble loss, making the generated adversarial example more likely to transfer to other models as it can fool multiple models concurrently. However, existing ensemble attacks simply fuse the outputs of the surrogate models evenly, thus are not efficacious to capture and amplify the intrinsic transfer information of adversarial examples. In this paper, we propose an adaptive ensemble attack, dubbed AdaEA, to adaptively control the fusion of the outputs from each model, via monitoring the discrepancy ratio of their contributions towards the adversarial objective. Furthermore, an extra disparity-reduced filter is introduced to further synchronize the update direction. As a result, we achieve considerable improvement over the existing ensemble attacks on various datasets, and the proposed AdaEA can also boost existing transfer-based attacks, which further demonstrates its efficacy and versatility.

Related papers

• Enhancing Adversarial Transferability with Adversarial Weight Tuning [36.09966860069978]
  adversarial examples (AEs) mislead the model while appearing benign to human observers. AWT is a data-free tuning method that combines gradient-based and model-based attack methods to enhance the transferability of AEs.
  arXiv  Detail & Related papers  (2024-08-18T13:31:26Z)
• SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation [56.622250514119294]
  In contrast to white-box adversarial attacks, transfer attacks are more reflective of real-world scenarios. We propose a self-augment-based transfer attack method, termed SA-Attack.
  arXiv  Detail & Related papers  (2023-12-08T09:08:50Z)
• Enhancing Adversarial Attacks: The Similar Target Method [6.293148047652131]
  adversarial examples pose a threat to deep neural networks' applications. Deep neural networks are vulnerable to adversarial examples, posing a threat to the models' applications and raising security concerns. We propose a similar targeted attack method named Similar Target(ST)
  arXiv  Detail & Related papers  (2023-08-21T14:16:36Z)
• Common Knowledge Learning for Generating Transferable Adversarial Examples [60.1287733223249]
  This paper focuses on an important type of black-box attacks, where the adversary generates adversarial examples by a substitute (source) model. Existing methods tend to give unsatisfactory adversarial transferability when the source and target models are from different types of DNN architectures. We propose a common knowledge learning (CKL) framework to learn better network weights to generate adversarial examples.
  arXiv  Detail & Related papers  (2023-07-01T09:07:12Z)
• Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples [89.85593878754571]
  transferability of adversarial examples across deep neural networks is the crux of many black-box attacks. We advocate to attack a Bayesian model for achieving desirable transferability. Our method outperforms recent state-of-the-arts by large margins.
  arXiv  Detail & Related papers  (2023-02-10T07:08:13Z)
• Frequency Domain Model Augmentation for Adversarial Attack [91.36850162147678]
  For black-box attacks, the gap between the substitute model and the victim model is usually large. We propose a novel spectrum simulation attack to craft more transferable adversarial examples against both normally trained and defense models.
  arXiv  Detail & Related papers  (2022-07-12T08:26:21Z)
• Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability [20.255708227671573]
  Black-box adversarial attacks can be transferred from one model to another. In this work, we propose a novel ensemble attack method called the variance reduced ensemble attack. Empirical results on the standard ImageNet demonstrate that the proposed method could boost the adversarial transferability and outperforms existing ensemble attacks significantly.
  arXiv  Detail & Related papers  (2021-11-21T06:33:27Z)
• Training Meta-Surrogate Model for Transferable Adversarial Attack [98.13178217557193]
  We consider adversarial attacks to a black-box model when no queries are allowed. In this setting, many methods directly attack surrogate models and transfer the obtained adversarial examples to fool the target model. We show we can obtain a Meta-Surrogate Model (MSM) such that attacks to this model can be easier transferred to other models.
  arXiv  Detail & Related papers  (2021-09-05T03:27:46Z)
• DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles [20.46399318111058]
  Adversarial attacks can mislead CNN models with small perturbations, which can effectively transfer between different models trained on the same dataset. We propose DVERGE, which isolates the adversarial vulnerability in each sub-model by distilling non-robust features. The novel diversity metric and training procedure enables DVERGE to achieve higher robustness against transfer attacks.
  arXiv  Detail & Related papers  (2020-09-30T14:57:35Z)
• Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution [83.02632136860976]
  We study black-box adversarial attacks against deep neural networks (DNNs) We develop a novel mechanism of adversarial transferability, which is robust to the surrogate biases. Experiments on benchmark datasets and attacking against real-world API demonstrate the superior attack performance of the proposed method.
  arXiv  Detail & Related papers  (2020-06-15T16:45:27Z)

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.