{"data":{"id":"d08247e4-a2f8-4156-b7c5-c5c9d5e5aff8","title":"Rethinking Fake Adversarial Examples for Single-Step Adversarial Training","summary":"Adversarial training (a method where AI models learn to defend against adversarial attacks, which are inputs designed to fool the model) typically requires expensive multi-step calculations, so researchers use single-step versions to save computing power. However, single-step methods create some misleading adversarial examples, called \"fakers,\" that actually weaken the model instead of strengthening it. This paper proposes FAST (Faker-Alleviating Single-step adversarial Training), which reduces the impact of fakers during training by dynamically adjusting how the model learns from difficult examples and introducing auxiliary samples to stabilize the training process.","solution":"The paper proposes FAST, which \"consists of two main components. First, it dynamically adjusts the label-smoothing level for adversarial examples according to their learning difficulty, making fakers easier for the model to learn. Second, it introduces an auxiliary sample with a weak adversarial effect, derived from the single-step adversarial example, which is used to dynamically ease the alignment with clean data and stabilize the optimization process.\" The code is available at https://github.com/mesunhlf/FAST.","labels":["research","safety"],"sourceUrl":"http://ieeexplore.ieee.org/document/11614558","publishedAt":"2026-07-20T13:17:07.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2026-07-20T13:17:07.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["safety"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.92,"researchCategory":"peer_reviewed","atlasIds":null}}