mlsanityData to 7 Oct 2026

SynthGuard: An Open Platform for Detecting AI-Generated Multimedia with Multimodal LLMs

arXiv 2511.12404 ↗First posted 16 Nov 2025cs.MM cs.AI cs.SDcited by 0

On the map: Multimodal LLMs / vision-language models → MLLM jailbreaks and adversarial robustness

Abstract (arXiv)

Artificial Intelligence (AI) has made it possible for anyone to create images, audio, and video with unprecedented ease, enriching education, communication, and creative expression. At the same time, the rapid rise of AI-generated media has introduced serious risks, including misinformation, identity misuse, and the erosion of public trust as synthetic content becomes increasingly indistinguishable from real media. Although deepfake detection has advanced, many existing tools remain closed-source, limited in modality, or lacking transparency and educational value, making it difficult for users to understand how detection decisions are made. To address these gaps, we introduce SynthGuard, an open, user-friendly platform for detecting and analyzing AI-generated multimedia using both traditional detectors and multimodal large language models (MLLMs). SynthGuard provides explainable inference, unified image and audio support, and an interactive interface designed to make forensic analysis accessible to researchers, educators, and the public. The SynthGuard platform is available at: https://in-engr-nova.it.purdue.edu/

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in MLLM jailbreaks and adversarial robustness, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 36 of its 45 references that are in the map. First posted on arXiv 16 Nov 2025.

Builds on

36 of 45 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Forensics-Bench: A Comprehensive Forgery Detection Benchmark Suite for Large Vision Language Models19 Mar

Other references · 35

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  3. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,151
  4. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  5. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  6. 2024How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suitescited by 1,033
  7. 2020Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Cluescited by 518
  8. 2023Towards Universal Fake Image Detectors that Generalize Across Generative Modelscited by 483
  9. 2021Generalizing Face Forgery Detection with High-frequency Featurescited by 261
  10. 2021Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domaincited by 241
  11. 2023UCF: Uncovering Common Features for Generalizable Deepfake Detectioncited by 177
  12. 2022CORE: Consistent Representation Learning for Face Forgery Detectioncited by 73
23 more
  1. 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 73
  2. 2024Preserving Fairness Generalization in Deepfake Detectioncited by 70
  3. 2020Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlightscited by 59
  4. 2021Eyes Tell All: Irregular Pupil Shapes Reveal GAN-generated Facescited by 48
  5. 2023On the Educational Impact of ChatGPT: Is Artificial Intelligence Ready to Obtain a University Degree?cited by 47
  6. 2023Improving Fairness in Deepfake Detectioncited by 40
  7. 2022GAN-generated Faces Detection: A Survey and New Perspectivescited by 29
  8. 2021Robust Attentive Deep Neural Network for Exposing GAN-generated Facescited by 28
  9. 2024AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmarkcited by 28
  10. 2024Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Imagescited by 20
  11. 2023Synthesizing Black-box Anti-forensics DeepFakes with High Visual Qualitycited by 12
  12. 2022Open-Eye: An Open Platform to Study Human Performance on Identifying AI-Synthesized Facescited by 11
  13. 2024Masked Conditional Diffusion Model for Enhancing Deepfake Detectioncited by 9
  14. 2023Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformationcited by 6
  15. 2023CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decompositioncited by 6
  16. 2024Improving Generalization for AI-Synthesized Voice Detectioncited by 6
  17. 2025Preserving AUC Fairness in Learning with Noisy Protected Groupscited by 6
  18. 2025Robust AI-Generated Face Detection with Imbalanced Datacited by 5
  19. 2023X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detectioncited by 4
  20. 2025Rethinking Individual Fairness in Deepfake Detectioncited by 4
  21. 2025Fit for Purpose? Deepfake Detection in the Real Worldcited by 4
  22. 2024DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detectioncited by 3
  23. 2024A Self-Learning Multimodal Approach for Fake News Detectioncited by 3

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  1. 2024Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics
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  3. 2025Methods and Trends in Detecting AI-Generated Images: A Comprehensive Review
  4. 2024LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models
  5. 2025Survey on AI-Generated Media Detection: From Non-MLLM to MLLM
  6. 2025AvatarShield: Visual Reinforcement Learning for Human-Centric Synthetic Video Detection
  7. 2025Generating and Detecting Various Types of Fake Image and Audio Content: A Review of Modern Deep Learning Technologies and Tools
  8. 2025BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation