Unifying Semantic Priors and High-Frequency Traces: Enhancing V-JEPA with Mixture-of-Experts for Robust Synthetic Image Forensics

arXiv:2609.16778 · cs.CV · Submitted 2026-09-15 · Read on arXiv

cs.CV

Submitted: 2026-09-15

Updated: 2026-09-16

Terminology

• Accuracy — 20× in this paper · explained in 1 episode
• Benchmark — 13× in this paper · explained in 2 episodes
• Transformer — 11× in this paper · explained in 2 episodes
• World Model — 7× in this paper · explained in 7 episodes
• Foundation Model — 6× in this paper · explained in 3 episodes
• Mixture-of-Experts (MoE) — 6× in this paper · explained in 7 episodes
• Trace — 6× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Gated Attention Multiple Instance Learning — 4× in this paper
• Joint-Embedding Predictive Architecture — 4× in this paper
• Latent Space — 4× in this paper · explained in 7 episodes
• Mixture of Experts — 4× in this paper · explained in 2 episodes
• Multiple Instance Learning (MIL) — 4× in this paper · explained in 2 episodes
• Residual Mixture-of-Experts — 4× in this paper
• Vision-language models — 4× in this paper · explained in 2 episodes
• Dual-Stream Architecture — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Abstraction — 2× in this paper · explained in 1 episode
• Adaptability — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Catastrophic Forgetting — 2× in this paper · explained in 5 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Multimodal Fusion — 2× in this paper · explained in 3 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Active Inference — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Multi Layer Perceptron — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes

Sources

• GPT-4 Technical Report
• Detecting Generated Images by Real Images Only
• AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• On DeepSeekMoE: Statistical Benefits of Shared Experts and Normalized Sigmoid Gating
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
• Gemini: A Family of Highly Capable Multimodal Models
• PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection

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