Watertox: The Art of Simplicity in Universal Attacks A Cross-Model Framework for Robust Adversarial Generation
Zhenghao Gao, Shengjie Xu, Meixi Chen, and Fangyao Zhao

TL;DR
Watertox is a simple yet effective universal adversarial attack framework that combines architectural diversity and precise perturbations to achieve high transferability and robustness across multiple models.
Contribution
It introduces a novel two-stage attack method using an ensemble of architectures and a voting mechanism for improved transferability and effectiveness.
Findings
Reduces model accuracy from 70.6% to 16.0% on targeted models.
Achieves up to 98.8% accuracy reduction on unseen architectures.
Outperforms state-of-the-art adversarial attack methods.
Abstract
Contemporary adversarial attack methods face significant limitations in cross-model transferability and practical applicability. We present Watertox, an elegant adversarial attack framework achieving remarkable effectiveness through architectural diversity and precision-controlled perturbations. Our two-stage Fast Gradient Sign Method combines uniform baseline perturbations () with targeted enhancements (). The framework leverages an ensemble of complementary architectures, from VGG to ConvNeXt, synthesizing diverse perspectives through an innovative voting mechanism. Against state-of-the-art architectures, Watertox reduces model accuracy from 70.6% to 16.0%, with zero-shot attacks achieving up to 98.8% accuracy reduction against unseen architectures. These results establish Watertox as a significant advancement in adversarial methodologies, with…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Advanced Malware Detection Techniques · Anomaly Detection Techniques and Applications
MethodsDropout · Softmax · Max Pooling · Dense Connections · ConvNeXt · Convolution
