Exploring Sparsity and Smoothness of Arbitrary $\ell_p$ Norms in Adversarial Attacks
This work addresses the problem of optimizing adversarial attack design for researchers and practitioners in machine learning security, though it is incremental as it builds on existing norm constraints by systematically exploring intermediate p values.
The paper tackled the problem of how the choice of the ℓ_p norm parameter p affects the sparsity and smoothness of adversarial attacks, showing that using p in [1.3, 1.5] yields the best trade-off between sparse and smooth attacks in experiments across multiple datasets and model architectures.
Adversarial attacks against deep neural networks are commonly constructed under $\ell_p$ norm constraints, most often using $p=1$, $p=2$ or $p=\infty$, and potentially regularized for specific demands such as sparsity or smoothness. These choices are typically made without a systematic investigation of how the norm parameter \( p \) influences the structural and perceptual properties of adversarial perturbations. In this work, we study how the choice of \( p \) affects sparsity and smoothness of adversarial attacks generated under \( \ell_p \) norm constraints for values of $p \in [1,2]$. To enable a quantitative analysis, we adopt two established sparsity measures from the literature and introduce three smoothness measures. In particular, we propose a general framework for deriving smoothness measures based on smoothing operations and additionally introduce a smoothness measure based on first-order Taylor approximations. Using these measures, we conduct a comprehensive empirical evaluation across multiple real-world image datasets and a diverse set of model architectures, including both convolutional and transformer-based networks. We show that the choice of $\ell_1$ or $\ell_2$ is suboptimal in most cases and the optimal $p$ value is dependent on the specific task. In our experiments, using $\ell_p$ norms with $p\in [1.3, 1.5]$ yields the best trade-off between sparse and smooth attacks. These findings highlight the importance of principled norm selection when designing and evaluating adversarial attacks.