DPG-Probe: Resolving the Semantic-Artifact Dilemma via Dual-Preserving Gradients for AIGI Forensics

The rapid evolution of Artificial Intelligence Generated Imagery presents severe forensic challenges characterized by two primary hurdles: the unprecedented fidelity and diversity of synthetic content, and the critical demand for robust, 'in-the-wild' real-world AIGI detection. To tackle these, current methods are largely based on advanced vision foundation models because they have already observed a vast visual world. However, this is precisely the fundamental reason why detection struggles to generalize. In the CLS space, real and fake images under the same paradigm theme are extremely close, meaning that when your test semantics deviate from the training set, the boundary structure moves with the object, rather than the intrinsic artifact. Addressing this limitation requires treating AIGI detection as a matter of provenance rather than standard semantic classification. Let $f$ represent a frozen VFM and $T$ a semantic-content and signal-structure preserving transformation (i.e., orthogonal rotations). While $f(x)$ and $f(T(x))$ should match on the core content of the image, any residual discrepancies are captured by hidden gradient directions ($g(x)$) sensitive to $T$. Therefore, we propose DPG-Probe, which leverages the residual discrepancies between $f(x)$ and $f(T(x))$ as a training-free, label-free probe. By taking a single backward pass of a self-consistency loss on intermediate CLS tokens through the frozen VFM, the resulting CLS gradients ($g(x)$) capture a structural common pattern of synthetic image formation rather than surface content. Because the gradient probe relies on dual-preserving transformations, it suppresses high-level semantic shifts and maintains superior stability under wild degradation compared to pixel-level artifacts (like VAE-reconstruction). Comprehensive evaluations demonstrate that IPG-Probe significantly enhances cross-domain generalization and real-world robustness over prior detectors.