CVPR2026 AIGC Detection AI paper notes paper summaries Image Inpainting Multimodal/VLM Self-Supervised Learning Speech & Audio Reasoning Watermarking

🔎 AIGC Detection¶

📷 CVPR2026 · 10 paper notes

📌 Same area in other venues: 🔬 ICLR2026 (30) · 💬 ACL2026 (17) · 🧪 ICML2026 (11) · 🤖 AAAI2026 (2) · 🧠 NeurIPS2025 (9) · 💬 ACL2025 (15)

Common Inpainted Objects In-N-Out of Context: The authors systematically replace objects in COCO images using Stable Diffusion inpainting to generate 97k "in-context / out-of-context" images of the exact same object. They then employ three 72B multimodal large models for consensus annotation of three-dimensional contextual labels ("location / size / co-occurrence"), building COinCO—the first inpainted forgery dataset with complete contextual annotations. Finally, they demonstrate three downstream tasks: fine-grained contextual classification, inferring objects from context, and training-free enhancement of SOTA forgery localization.
Enabling Supervised Learning of Generative Signatures for Generalized AI-Generated Images Detection: To address the deadlock where "generative traces in AI-generated images lack clean pairs and cannot be extracted via supervised learning," this paper uses a randomly-structured image reconstructor to artificially "create traces" on real images. The reconstruction residuals are treated as pseudo-labels to train a generative signature (GenSign) extractor, followed by a GenSign + RGB dual-stream classifier for detection, achieving SOTA cross-model generalization across four benchmarks.
Fine-grained Image Aesthetic Assessment: Learning Discriminative Scores from Relative Ranks: This work defines the new task of "Fine-grained Image Aesthetic Assessment" and constructs the FGAesthetics benchmark containing 32,217 images across 10,028 series. It proposes the FGAesQ model, which learns discriminative aesthetic scores from relative ranks through Difference-Preserving Tokenization (DiffToken), Contrastive Text-aligned Alignment (CTAlign), and Rank-Aware Regression (RankReg). The model achieves an accuracy of 0.779 in fine-grained scenarios while maintaining a coarse-grained SRCC of 0.770.
Inconsistency-aware Multimodal Schrodinger Bridge for Deepfake Localization: IaMSB reformulates "temporal interval localization" of audio-visual deepfakes as a Schrödinger Bridge (SB) generation problem—directly reading cross-modal consistency scores from the bridge's transmission cost and asymmetrically allocating computation steps to the more suspicious modality, resulting in a 3-10% gain over existing methods on strict IoU ([email protected]).
Investigating Self-Supervised Representations for Audio-Visual Deepfake Detection: This is a systematic "investigation" paper: the authors freeze 12 off-the-shelf self-supervised encoders (audio, visual, and audio-visual) and train only a single-layer linear probe on top of them. They evaluate their capability in audio-visual deepfake detection horizontally across three dimensions: "detection effectiveness, interpretability, and cross-modal complementarity". They find that "audio-driven" representations generalize best (with BRAVEn's visual encoder achieving SOTA), whereas the main difficulty in real-world data stems from the intrinsic complexity of the datasets themselves rather than features exploiting shallow shortcuts.
Learning Forgery-Aware Lip Representations Without Forgery Priors: To address the vulnerability of speaker authentication systems to personalized Talking Face Generation (TFG) forgeries, this paper proposes a detector trained solely on real videos without relying on any forgery samples. By combining mixed-fake lip generation, asymmetric contrastive learning, and Gaussian regularization, the real lip motion features are compressed into a compact hypersphere. Anything outside the sphere (forgeries and impostors) is treated as an outlier, reducing the error rate by over 10% against 8 modern forgeries compared to 10 SOTA methods.
Learning Where to Look and How to Judge: Resolution-agnostic Image Quality Assessment with Quality-aware Saliency: To address four common issues in No-Reference Image Quality Assessment (NR-IQA)—forced resizing to accommodate pre-trained resolutions, poor generalization across resolutions, difficulty in joint training due to inconsistent MOS scales, and computational explosion for UHD images—this paper proposes ReLIQS. It samples fixed-size patches from the original resolution and its scaled variants, encoding them with CLIP. A lightweight "Perceptual Importance Estimator (PIE)" learns IQA-specific saliency to select a few key patches, while a "Latent Quality Axis Module (LQAM)" aggregates multi-scale embeddings into a single score. ReLIQS outperforms CNN, CLIP, and MLLM-based baselines across various real/synthetic/AIGC distortions and resolutions with lower computational cost.
Locate-Then-Examine: Grounded Region Reasoning Improves Detection of AI-Generated Images: LTE enables Vision-Language Models to first perform a "global scan to locate suspicious regions" and then "zoom in and crop to re-examine for the final verdict." It upgrades one-time classification into a two-stage region-grounded reasoning process. Accompanied by the TRACE dataset containing box-level annotations and forensic explanations, it achieves simultaneous improvements in accuracy, robustness, and interpretability.
NOWA: Null-space Optical Watermark for Invisible Capture Fingerprinting and Tamper Localization: A learnable phase mask is inserted at the camera aperture to encode the authentication signal into the null-space of the imaging operator (rendering it completely invisible during capture). A measurement-consistent Null-Space Network (NSN) is then utilized to reconstruct high-quality images and anchor this watermark. Tampering disrupts the statistical structure in the null-space projection, allowing the detector to localize the changes at the pixel level. Under AIGC editing, the F1 score exceeds EditGuard (0.993 vs 0.97), and the system is inherently unforgeable to unknown counterfeiters.
PPM-CLIP: Probabilistic Prompt Modeling for Generalizable AI-Generated Image Detection: PPM-CLIP replaces the "discriminative static boundary" paradigm with "generative probabilistic inference." It utilizes normalizing flows to generate a family of adaptive prompts (multiple hypotheses) for each image and determines the results by averaging cosine similarities to marginalize noise. Combined with frequency-guided patch-wise contrastive learning, it forces the CLIP encoder to capture high-frequency forgery traces, significantly outperforming SOTA in cross-generator generalization on Ojha, GenImage, and DRCT.