Adv-TGD: Adversarial Text-Guided Diffusion for Face Recognition Impersonation Attacks
The widespread adoption of face recognition (FR) technologies raises serious privacy concerns, as facial data can be exploited without consent. To address this challenge, we propose Adv-TGD, a generative adversarial attack framework that synthesizes photorealistic faces capable of impersonating target identities and deceiving face recognition systems. Built upon Stable Diffusion v2.1, Adv-TGD performs per-sample LoRA fine-tuning conditioned on concise textual prompts to generate natural yet adversarially manipulated identities. Unlike conventional identity attack approaches, our method optimizes lightweight cross-attention adapters for each source-target pair within a fixed-timestep denoi...
| Authors | Omid Ahmadieh, Nima Karimian |
|---|---|
| Primary category | cs.CV |
| Published | 10 June 2026 |
| Last updated at source | 16 June 2026 |
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