Almost for Free: Crafting Adversarial Examples with Convolutional Image Filters

• URL: http://arxiv.org/abs/2605.01098v1
• Date: Fri, 01 May 2026 21:08:09 GMT
• Title: Almost for Free: Crafting Adversarial Examples with Convolutional Image Filters
• Abstract summary: Adversarial examples in machine learning are typically generated using gradients, obtained either directly through access to the model or approximated via queries to it.<n>In this paper, we propose a much simpler approach to craft adversarial examples, drawing inspiration from insights of explainable machine learning.<n>We design emphadversarial image filters that are based on classic edge detection algorithms but optimized to deceive learning models.<n>The resulting untargeted attacks are transferable and require only a single pass over the input.
• Score: 50.23399491814351
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adversarial examples in machine learning are typically generated using gradients, obtained either directly through access to the model or approximated via queries to it. In this paper, we propose a much simpler approach to craft adversarial examples, drawing inspiration from insights of explainable machine learning. In particular, we design \emph{adversarial image filters} that are based on classic edge detection algorithms but optimized to deceive learning models. The resulting untargeted attacks are transferable and require only a single pass over the input. Empirically, we find that 3x3 filters already enable success rates between 30% and 80% on different neural networks. Compared to related approaches using generative models for crafting adversarial examples, we reduce the number of parameters by five orders of magnitude, resulting in a very efficient attack. When investigating the parameters of the learned filters, we observe interesting properties such as a high transferability between models and structures common to classic image filters. Our results provide further insights into the vulnerability of neural networks and their fragility to malicious noise.

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