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Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack

Zhibo Jin, Jiayu Zhang, Zhiyu Zhu, Chenyu Zhang, Jiahao Huang, Jianlong Zhou, Fang Chen

TL;DR

Addressing a gap in adversarial attacks, the paper treats attack generation as a two-phase process (DSP and DOP) and shows that slight, principled fine-tuning of model parameters can amplify adversarial effectiveness. It introduces Parameter Adaptive Adversarial Attack (P3A), with four parameter-update rules and supporting theory based on directional finite difference for the second-order term $\partial^2 L / (\partial x \partial \theta)$ to justify gain in loss. Through extensive white-box and black-box experiments across CNNs and vision transformers, P3A yields higher loss increases $L$ and larger attack success rates, improving transferability relative to baselines. The work provides rigorous definitions, ablation studies, and open-source code to enable broader adoption and further study of parameter-aware adversarial strategies.

Abstract

In recent times, the swift evolution of adversarial attacks has captured widespread attention, particularly concerning their transferability and other performance attributes. These techniques are primarily executed at the sample level, frequently overlooking the intrinsic parameters of models. Such neglect suggests that the perturbations introduced in adversarial samples might have the potential for further reduction. Given the essence of adversarial attacks is to impair model integrity with minimal noise on original samples, exploring avenues to maximize the utility of such perturbations is imperative. Against this backdrop, we have delved into the complexities of adversarial attack algorithms, dissecting the adversarial process into two critical phases: the Directional Supervision Process (DSP) and the Directional Optimization Process (DOP). While DSP determines the direction of updates based on the current samples and model parameters, it has been observed that existing model parameters may not always be conducive to adversarial attacks. The impact of models on adversarial efficacy is often overlooked in current research, leading to the neglect of DSP. We propose that under certain conditions, fine-tuning model parameters can significantly enhance the quality of DSP. For the first time, we propose that under certain conditions, fine-tuning model parameters can significantly improve the quality of the DSP. We provide, for the first time, rigorous mathematical definitions and proofs for these conditions, and introduce multiple methods for fine-tuning model parameters within DSP. Our extensive experiments substantiate the effectiveness of the proposed P3A method. Our code is accessible at: https://anonymous.4open.science/r/P3A-A12C/

Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack

TL;DR

Addressing a gap in adversarial attacks, the paper treats attack generation as a two-phase process (DSP and DOP) and shows that slight, principled fine-tuning of model parameters can amplify adversarial effectiveness. It introduces Parameter Adaptive Adversarial Attack (P3A), with four parameter-update rules and supporting theory based on directional finite difference for the second-order term to justify gain in loss. Through extensive white-box and black-box experiments across CNNs and vision transformers, P3A yields higher loss increases and larger attack success rates, improving transferability relative to baselines. The work provides rigorous definitions, ablation studies, and open-source code to enable broader adoption and further study of parameter-aware adversarial strategies.

Abstract

In recent times, the swift evolution of adversarial attacks has captured widespread attention, particularly concerning their transferability and other performance attributes. These techniques are primarily executed at the sample level, frequently overlooking the intrinsic parameters of models. Such neglect suggests that the perturbations introduced in adversarial samples might have the potential for further reduction. Given the essence of adversarial attacks is to impair model integrity with minimal noise on original samples, exploring avenues to maximize the utility of such perturbations is imperative. Against this backdrop, we have delved into the complexities of adversarial attack algorithms, dissecting the adversarial process into two critical phases: the Directional Supervision Process (DSP) and the Directional Optimization Process (DOP). While DSP determines the direction of updates based on the current samples and model parameters, it has been observed that existing model parameters may not always be conducive to adversarial attacks. The impact of models on adversarial efficacy is often overlooked in current research, leading to the neglect of DSP. We propose that under certain conditions, fine-tuning model parameters can significantly enhance the quality of DSP. For the first time, we propose that under certain conditions, fine-tuning model parameters can significantly improve the quality of the DSP. We provide, for the first time, rigorous mathematical definitions and proofs for these conditions, and introduce multiple methods for fine-tuning model parameters within DSP. Our extensive experiments substantiate the effectiveness of the proposed P3A method. Our code is accessible at: https://anonymous.4open.science/r/P3A-A12C/
Paper Structure (28 sections, 3 theorems, 17 equations, 8 figures, 1 table)

This paper contains 28 sections, 3 theorems, 17 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related work
  3. White-box Attacks
  4. Black-box Attacks
  5. Adversarial Defenses
  6. Method
  7. Problem Definition
  8. DSP and DOP in Adversarial Attacks
  9. Second-Order Partial Derivatives and DFD
  10. Parameter Adaptive Adversarial Attack
  11. Experiments
  12. Experimental Setup
  13. Dataset
  14. Models
  15. Baseline Methods
  16. ...and 13 more sections

Key Result

Theorem 1

The second-order derivative of a function can be estimated using the difference in first-order derivatives. Given a function $f(v,u)$ with two variables, we have

Figures (8)

  • Figure 1: Diagram of Adversarial Attack Process with DSP and DOP Components
  • Figure 2: Incremental Efficacy of the P3A Method on Cross Entropy Loss Across Attack Steps. The y-axis represents the change in cross-entropy loss, and the x-axis represents attack steps. The black dashed line means the efficacy of attack methods without P3A.
  • Figure 3: Incremental Efficacy of the P3A Method on KL Loss Across Attack Steps. The y-axis represents the change in KL loss, and the x-axis represents attack steps. The black dashed line means the efficacy of attack methods without P3A.
  • Figure 4: Incremental Efficacy of the P3A Method on ASR Across Attack Steps. The y-axis represents the change in ASR, and the x-axis represents attack steps. The black dashed line means the efficacy of attack methods without P3A.
  • Figure 5: Performance of P3A on various models under 4 different parameter updates methods. The left y-axis represents the change in Cross Entropy Loss, and the right y-axis represents the change in ASR
  • ...and 3 more figures

Theorems & Definitions (3)

  • Theorem 1: Directional Finite Difference (DFD)
  • Theorem 2
  • Theorem 3