{"data":{"id":"be8ad487-fceb-44aa-b88f-ac93da15dd55","title":"HENet: A Heterogeneous Encoding Network for General and Robust Adversarial Example Generation","summary":"HENet, a dual-branch generative model, is proposed to build a robust generator-based adversarial attack framework against deep neural networks. Its Adaptive Feature Fusion Module addresses dimension and representativeness mismatches between CNNs and Transformers, while a Dynamic Differentiable JPEG Simulator uses an adaptive quantization mask to improve robustness under JPEG compression. The authors report a better attack success rate, lower perturbation magnitude and higher robustness across target architectures.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11480207","publishedAt":"2026-04-13T13:17:12.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-04-13T13:17:12.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.93,"researchCategory":"peer_reviewed","atlasIds":null}}