mlsanityData to 7 Oct 2026

DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection

arXiv 2306.01272 ↗First posted 2 Jun 2023cs.CV cs.CR cs.LGcited by 9

On the map: LLM safety and security → Watermarking generated content and models

Abstract (arXiv)

The tremendous recent advances in generative artificial intelligence techniques have led to significant successes and promise in a wide range of different applications ranging from conversational agents and textual content generation to voice and visual synthesis. Amid the rise in generative AI and its increasing widespread adoption, there has been significant growing concern over the use of generative AI for malicious purposes. In the realm of visual content synthesis using generative AI, key areas of significant concern has been image forgery (e.g., generation of images containing or derived from copyright content), and data poisoning (i.e., generation of adversarially contaminated images). Motivated to address these key concerns to encourage responsible generative AI, we introduce the DeepfakeArt Challenge, a large-scale challenge benchmark dataset designed specifically to aid in the building of machine learning algorithms for generative AI art forgery and data poisoning detection. Comprising of over 32,000 records across a variety of generative forgery and data poisoning techniques, each entry consists of a pair of images that are either forgeries / adversarially contaminated or not. Each of the generated images in the DeepfakeArt Challenge benchmark dataset footnoteThe link to the dataset: http://anonforreview.com has been quality checked in a comprehensive manner.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security. Builds on 23 of its 38 references that are in the map. First posted on arXiv 2 Jun 2023.

Builds on

23 of 38 references.

References · 23

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11 more
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Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 6; 2025: 2.

9 papers.

  1. 2024Replication in Visual Diffusion Models: A Survey and Outlookcited by 11
  2. 2024MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasetscited by 10
  3. 2023VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Modelscited by 5
  4. 2024The last Dance : Robust backdoor attack via diffusion models and bayesian approachcited by 3
  5. 2024Unveiling the Truth: Exploring Human Gaze Patterns in Fake Imagescited by 3
  6. 2024LookupForensics: A Large-Scale Multi-Task Dataset for Multi-Phase Image-Based Fact Verificationcited by 2
  7. 2024Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AIcited by 1
  8. 2025UniAIDet: A Unified and Universal Benchmark for AI-Generated Image Content Detection and Localizationcited by 1
  9. 2025DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Artcited by 0