Synthetic Image Detection with CLIP: Understanding and Assessing Predictive Cues

arXiv:2602.12381 · cs.CV · Submitted 2026-02-12 · Read on arXiv

cs.CV

Submitted: 2026-02-12

Updated: 2026-08-15

Code: https://github.com/marco-willi/clip-cues

Terminology

• Generative Model — 21× in this paper · explained in 2 episodes
• Generative Models — 20× in this paper · explained in 2 episodes
• Diffusion Model — 14× in this paper · explained in 8 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Generalization — 13× in this paper · explained in 13 episodes
• Embeddings — 12× in this paper · explained in 2 episodes
• Diffusion Models — 11× in this paper · explained in 6 episodes
• Regularization — 11× in this paper · explained in 3 episodes
• Sparsity — 11× in this paper · explained in 4 episodes
• Synthetic Image Detection — 11× in this paper
• Accuracy — 7× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 6× in this paper
• Robustness — 6× in this paper · explained in 5 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Distribution Shift — 4× in this paper · explained in 5 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Adversarial Attacks — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Latent Diffusion Models — 3× in this paper · explained in 2 episodes
• Provenance — 3× in this paper · explained in 3 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Adversarial Robustness — 2× in this paper · explained in 3 episodes
• Computer Vision and Pattern Recognition Workshops — 2× in this paper
• Failure Modes — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• International Workshop on Information Forensics and Security — 2× in this paper
• AUC — 1× in this paper · explained in 2 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Challenge on Learned Image Compression — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Image Quality Assessment — 1× in this paper
• Linear Probing — 1× in this paper · explained in 2 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Online Adaptation — 1× in this paper · explained in 3 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• GenAI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusions
• Reproducible scaling laws for contrastive language-image learning
• Are CLIP features all you need for Universal Synthetic Image Origin Attribution?
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection
• Raising the Bar of AI-generated Image Detection with CLIP
• Density estimation using Real NVP
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
• Improving Interpretability and Robustness for the Detection of AI-Generated Images
• Interpreting CLIP's Image Representation via Text-Based Decomposition
• Denoising Diffusion Probabilistic Models
• ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale
• Imagen 3
• CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection
• Auto-Encoding Variational Bayes
• Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection
• Flow Matching for Generative Modeling
• Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images
• Towards Universal Fake Image Detectors that Generalize Across Generative Models

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