# SAIF: Sparse Adversarial and Imperceptible Attack Framework Source: [SAIF: Sparse Adversarial and Imperceptible Attack Framework]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:36:41.717Z Authors: Tooba Imtiaz, Morgan Kohler, Jared Miller, Zifeng Wang, Masih Eskandar, Mario Sznaier, Octavia Camps, Jennifer Dy Published: 2022-12-14T20:28:50.000Z ## Abstract Adversarial attacks hamper the decision-making ability of neural networks by perturbing the input signal. The addition of calculated small distortion to images, for instance, can deceive a well-trained image classification network. In this work, we propose a novel attack technique called Sparse Adversarial and Interpretable Attack Framework (SAIF). Specifically, we design imperceptible attacks that contain low-magnitude perturbations at a small number of pixels and leverage these sparse attacks to reveal the vulnerability of classifiers. We use the Frank-Wolfe (conditional gradient) algorithm to simultaneously optimize the attack perturbations for bounded magnitude and sparsity with $O(1/\\sqrt{T})$ convergence. Empirical results show that SAIF computes highly imperceptible and interpretable adversarial examples, and outperforms state-of-the-art sparse attack methods on the ImageNet dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()