A comprehensive study of cross-domain adversarial robustness and attack transferability in image-based malware detection and classification

Summary

This paper presents a framework for evaluating adversarial robustness and attack transferability in image-based deep learning models for malware detection and classification. The authors apply image-domain attacks from FGSM to AutoAttack and test whether binary-domain manipulations remain effective after conversion to an image representation. They report average attack success rates of 64.6% for FGSM and 98.8% for AutoAttack, and accuracy drops of up to 42% from transferred binary-domain manipulations.

Topics

• Adversarial machine learning

Related items

• InfoDoes Target Alignment Mean Target Recovery? An Evidence-Ladder Study of Adversarial Claims on Contrastive EncodersSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoBRANCH: Bypassing Multi-Scanner AI GuardrailsSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoDetecting Adversarial Images through Response Profiles of Vision-Language ModelsSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoGraphRectify: Graph-Based Transfer of Adversarial Example Detectors Across Neural NetworksSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoVCR-Bench: A Modular Open-Source Benchmark for Video Classification RobustnessSimilar attack · Arxiv (cs.RO + cs.CV security)