Trustworthy synthetic visual media: Evidence across the media lifecycle

arXiv:2609.34232 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Diffusion Model — 41× in this paper · explained in 8 episodes
• Verification — 40× in this paper · explained in 2 episodes
• Provenance — 38× in this paper · explained in 3 episodes
• Diffusion Models — 37× in this paper · explained in 6 episodes
• Benchmark — 34× in this paper · explained in 2 episodes
• Accuracy — 31× in this paper · explained in 1 episode
• Verifier — 24× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 22× in this paper
• Robustness — 15× in this paper · explained in 5 episodes
• Calibration — 10× in this paper · explained in 7 episodes
• Accountability — 9× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 9× in this paper
• Trace — 9× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 7× in this paper
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• International Conference on Machine Learning — 6× in this paper
• Latent Diffusion Model — 6× in this paper · explained in 2 episodes
• Latent Diffusion Models — 6× in this paper · explained in 2 episodes
• Generative AI — 5× in this paper · explained in 2 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• World Model — 5× in this paper · explained in 7 episodes
• AUC — 4× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Data Provenance — 4× in this paper · explained in 2 episodes
• In European Conference on Computer Vision — 4× in this paper
• Personalization — 4× in this paper · explained in 2 episodes
• Reconstruction Error — 4× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Abstention — 2× in this paper · explained in 1 episode
• Actor — 2× in this paper · explained in 3 episodes
• Adversarial Attacks — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• In IEEE International Conference on Image Processing — 2× in this paper
• International Workshop on Information Forensics and Security — 2× in this paper
• Latent Space — 2× in this paper · explained in 7 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Adversarial Model — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition Workshops — 1× in this paper
• Cooperation — 1× in this paper · explained in 2 episodes
• Cross-Model Transfer — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fingerprinting — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• IEEE International Conference on Multimedia and Expo — 1× in this paper
• Meta-learning — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Secure and Robust Watermarking for AI-generated Images: A Comprehensive Survey
• Methods and Trends in Detecting AI-Generated Images: A Comprehensive Review
• GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
• Hierarchical Text-Conditional Image Generation with CLIP Latents
• Qwen-Image Technical Report
• WaveNet: A Generative Model for Raw Audio
• Jukebox: A Generative Model for Music
• AudioGen: Textually Guided Audio Generation
• MusicLM: Generating Music From Text
• AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
• Imagen Video: High Definition Video Generation with Diffusion Models
• Make-A-Video: Text-to-Video Generation without Text-Video Data
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• Movie Gen: A Cast of Media Foundation Models
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• Seedance 1.0: Exploring the Boundaries of Video Generation Models
• World Models
• DreamFusion: Text-to-3D using 2D Diffusion
• Point-E: A System for Generating 3D Point Clouds from Complex Prompts

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