Query-Efficient Hard-Label Attacks Against Black-Box Image Forgery Localization Model via Reinforcement Learning

Summary

Researchers propose AdvFor, a query-efficient black-box attack for image forgery localization models that only return hard, mask-only binary outputs. The method learns a transferable attack policy with reinforcement learning, formulated as a finite-horizon Markov Decision Process, and uses a fixed budget of T=7 queries per image. Experiments on six benchmark datasets and multiple modern localization models show it outperforms representative baselines under the same perturbation constraints, including under deployment-style defenses.

Related items

• InfoEvaluating the robustness of explainable AI methods to adversarial attacks in intrusion and malware detectionSimilar attack · OpenAlex (peer-reviewed AI security)
• InfoRL-ATTACK: A Generalized Black-Box Adversarial Attack on Language Models via Reinforcement LearningSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoEvaluating Parameter-Efficient Fine-Tuning Attacks Against Non-Transferable Learning DefensesSimilar attack · Elsevier Security Journals
• InfoEscalate or Close: How Fast-Tier Models Handle Held-Out Web Attack Families in Two-Tier TriageSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoVIPA: View-Invariant Projector-Based Adversarial AttackSimilar attack · Arxiv (cs.RO + cs.CV security)