Architecture-Agnostic Feature Synergy for Universal Defense Against Heterogeneous Generative Threats

Researchers propose ATFS, a new framework that provides universal defense against multiple generative AI architectures simultaneously, overcoming limitations of current defense mechanisms that only work against specific AI models. The system achieves over 90% protection effectiveness within 40 iterations and works across different generative models including Diffusion Models, GANs, and VQ-VAE.

• →Current AI defense mechanisms create "defense silos" that only protect against specific generative model architectures.
• →ATFS framework solves gradient interference problems by aligning feature representations across different AI architectures.
• →The system achieves over 90% protection performance within 40 iterations and maintains effectiveness under tight perturbation budgets.
• →ATFS extends seamlessly to unseen architectures and demonstrates robust resistance to JPEG compression and scaling.
• →The framework is computationally efficient and open-sourced, offering a pathway to universal generative AI security.