Boosting Adversarial Attacks with Momentum (1710.06081v3)

Abstract: Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences. Adversarial attacks serve as an important surrogate to evaluate the robustness of deep learning models before they are deployed. However, most of existing adversarial attacks can only fool a black-box model with a low success rate. To address this issue, we propose a broad class of momentum-based iterative algorithms to boost adversarial attacks. By integrating the momentum term into the iterative process for attacks, our methods can stabilize update directions and escape from poor local maxima during the iterations, resulting in more transferable adversarial examples. To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks. We hope that the proposed methods will serve as a benchmark for evaluating the robustness of various deep models and defense methods. With this method, we won the first places in NIPS 2017 Non-targeted Adversarial Attack and Targeted Adversarial Attack competitions.

• The paper presents the MI-FGSM method that integrates momentum to stabilize updates and avoid local maxima in adversarial example generation.
• It demonstrates that incorporating momentum enhances both white-box and black-box attack success rates through ensemble strategies.
• Experimental results on ImageNet confirm improved attack transferability, highlighting critical security vulnerabilities in DNNs.

An Overview of Momentum-Based Adversarial Attack Optimization

The manuscript "Boosting Adversarial Attacks with Momentum" explores a novel class of momentum-centric iterative algorithms engineered to enhance the efficacy of adversarial attacks against deep neural networks (DNNs). The purpose of these attacks is to evaluate the robustness of deep learning models prior to their deployment, recognizing the pervasive security threats posed by adversarial examples. While the vulnerability of DNNs to adversarial examples is acknowledged, the success rate of prevailing attack methods on black-box models often remains suboptimal. This paper introduces techniques aimed at rectifying this deficiency, chiefly by integrating a momentum term into iterative processes.

Methodological Advances

Momentum iterative methodologies, specifically the momentum iterative fast gradient sign method (MI-FGSM), underpin the proposed approach. Traditionally, iterative gradient-based techniques progress by perturbing inputs incrementally to maximize a loss function. The introduction of a momentum term accumulates gradient velocity vectors over iterations, thereby stabilizing updates and avoiding inferior local maxima, resulting in adversarial examples that exhibit superior transferability across models. This paradigm notably mitigates the trade-off characterizing attack potency versus transferability, accomplishing higher success rates in both white- and black-box attacks.

An extension of the ensemble attack strategy further bolsters black-box attack efficacy. By orchestrating attacks across multiple models, the ensemble method capitalizes on cross-model transferability—the notion that adversarial examples remain potent across varying architectures due to similar decision boundary learning patterns. The paper contrasts alternative ensemble mechanisms, concluding that leveraging logit-based ensembles maximizes the transferability and success rate of adversarial examples, thereby enhancing model vulnerability assessments.

Experimental Insights and Implications

Empirical validation is executed using networks trained on the ImageNet dataset, highlighting profound discrepancies between momentum-integrated techniques and conventional methods. MI-FGSM achieves consistent improvements in attack success rates compared to one-step and vanilla iterative methods, reinforcing its robustness as a white-box model adversary while safeguarding transferability capabilities. Notably, the momentum term considerably elevates black-box attack proficiency by sustaining optimal update directions. Through ensemble-based attacks, even adversarially trained models, reputed for their resilience, succumb to a substantial proportion of these attacks, reflecting emergent security concerns.

Theoretical and Practical Speculations

The implications delineated in the paper are comprehensive. Momentum-based advancements not only present new benchmarks for evaluating and fortifying model robustness but also necessitate the exploration of countermeasures against increasingly sophisticated attack vectors. The findings suggest potential enhancements in neural network training methodologies, replicating benefits commonly associated with momentum optimization such as improved generalization, extending its influence to adversarial contexts.

Furthermore, the paper culminated in accolades, securing first-place standings in the NIPS 2017 competitions, underscoring the pragmatic applicability and competitive edge of the proposed methods.

Future Trajectories

As adversarial landscape complexity escalates, future research is poised to build upon these foundational advancements. Potential directions include refining momentum applications and ensemble strategies, further dissecting their underlying mechanisms, and formulating robust defense architectures against diversified attack modalities. The persistent evolution of these methodologies signals opportunities for continually safeguarding AI systems amidst escalating adversarial dynamics.