Need We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation
arXiv:2606.07660v1 Announce Type: cross Abstract: Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations. Under optimization on limited samples, models overfit to local domain shortcuts. Fine-tuning massive weights on specialized data introduces erroneous inductive biases, inducing a measurable $\mathcal{L}_2$ norm perturbation in the high-dimensional feature space -- a phenomenon we formalize as anchor drift. Amplified by nonlinear activations, this drift impairs zero-shot forgery detection across unseen domains.We prop
The proliferation of generative AI necessitates robust and efficient methods for artifact detection, and this research proposes a novel gradient-free approach.
This research provides a method for generative artifact detection that does not compromise foundation model integrity, crucial for maintaining trust and reliability in AI-generated content.
The ability to detect generative artifacts without fine-tuning could lead to more stable and adaptable detection systems, improving the robustness of AI safety measures.
- · AI safety researchers
- · Trust & safety platforms
- · Foundation model developers
- · Malicious generative AI users
- · Current gradient-based detection methods
Improved detection of AI-generated content, bolstering efforts against misinformation and deepfakes.
Reduced need for extensive fine-tuning datasets, making artifact detection more scalable and less resource-intensive.
Enhanced public trust in digital media as the ability to discern AI-generated content improves, potentially leading to new regulatory frameworks for content authenticity.
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Read at arXiv cs.LG