{"data":{"id":"9baa8d35-5c64-4b75-b8a5-4e8e8c32d263","title":"MF2DA: Multi-Level Feature Fusion for Robust Detection and Attribution of Universal AI-Generated Images","summary":"AI-generated images that look very realistic are becoming a major problem for information trustworthiness and accountability, and current detection methods struggle with three main issues: they fail when images are compressed on social media, they don't work well on harmful content, and they can't identify which AI model created the image. This paper proposes MF2DA, a system that combines multiple AI techniques (including edge detection for pixel-level artifacts and CLIP-ViT, a model trained to understand both images and text) to both detect AI-generated images and identify which generator created them, even after social media compression.","solution":"N/A -- no mitigation discussed in source.","labels":["research","safety"],"sourceUrl":"http://ieeexplore.ieee.org/document/11653434","publishedAt":"2026-08-12T13:16:39.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":["CLIP","ResNet"],"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":"2026-08-12T13:16:39.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["integrity","safety"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}