arXiv:2606.11615v1 Announce Type: cross Abstract: 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, Adv-TGD performs per-sample LoRA fine-tuning conditioned on concise textual prompts to generate natural yet adversarially manipulated identities. Unlike conventional i
The proliferation of face recognition technology necessitates immediate attention on its vulnerabilities as AI models like Stable Diffusion become more capable of generating realistic media.
This research highlights emerging security risks in biometric systems, threatening the integrity and trustworthiness of identity verification methods reliant on facial recognition. It underscores the dual-use nature of generative AI, capable of enabling both beneficial and malicious applications.
The ease with which photorealistic faces can be generated to impersonate identities is increasing, potentially undermining existing face recognition security protocols.
- · Adversarial AI researchers
- · Cybersecurity consultancies
- · Developers of robust anti-spoofing technologies
- · Face recognition system vendors
- · Organizations relying solely on face recognition for security
- · Individuals whose identities can be impersonated
Existing face recognition systems face immediate challenges in distinguishing real faces from AI-generated impersonations.
Increased investment in advanced anti-spoofing and liveness detection technologies will become critical for biometric security.
Public trust in face recognition for secure authentication may erode, leading to a demand for multi-modal biometric security or alternative verification methods.
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