σ-zero: Gradient-based Optimization of ℓ0-norm Adversarial Examples
Antonio Emanuele Cinà Francesco Villani Maura Pintor Lea Schönherr Battista Biggio Marcello Pelillo
Evaluating the adversarial robustness of deep networks to gradient-based attacks is challenging.
While most attacks consider $\ell_2$- and $\ell_\infty$-norm constraints to craft input perturbations, only a few investigate sparse $\ell_1$- and $\ell_0$-norm attacks.
In particular, $\ell_0$-norm attacks remain the least studied due to the inherent complexity of optimizing over a non-convex and non-differentiable constraint.
However, evaluating adversarial robustness under these attacks could reveal weaknesses otherwise left untested with more conventional $\ell_2$- and $\ell_\infty$-norm attacks.
In this work, we propose a novel $\ell_0$-norm attack, called $\sigma$-zero, which leverages a differentiable approximation of the $\ell_0$ norm to facilitate gradient-based optimization, and an adaptive projection operator to dynamically adjust the trade-off between loss minimization and perturbation sparsity.
Extensive evaluations using MNIST, CIFAR10, and ImageNet datasets, involving robust and non-robust models, show that $\sigma$-zero finds minimum $\ell_0$-norm adversarial examples without requiring any time-consuming hyperparameter tuning, and that it outperforms all competing sparse attacks in terms of success rate, perturbation size, and efficiency.
adversarial examples, sparse attacks, gradient-based attack, machine learning security, evasion attacks, sparse optimization
The $\sigma$-zero attack uses a differentiable approximation of the $\ell_0$ norm for gradient-based optimization, with an adaptive projection operator that balances loss minimization and perturbation sparsity.