Synthetic Photography Detection: A Visual Guidance for Identifying Synthetic Images Created by AI
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Abstract (arXiv)
Artificial Intelligence (AI) tools have become incredibly powerful in generating synthetic images. Of particular concern are generated images that resemble photographs as they aspire to represent real world events. Synthetic photographs may be used maliciously by a broad range of threat actors, from scammers to nation-state actors, to deceive, defraud, and mislead people. Mitigating this threat usually involves answering a basic analytic question: Is the photograph real or synthetic? To address this, we have examined the capabilities of recent generative diffusion models and have focused on their flaws: visible artifacts in generated images which reveal their synthetic origin to the trained eye. We categorize these artifacts, provide examples, discuss the challenges in detecting them, suggest practical applications of our work, and outline future research directions.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 16 of its 35 references that are in the map. First posted on arXiv 12 Aug 2024.
Builds on
16 of 35 references.
References · 16
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
- 2019CNN-generated images are surprisingly easy to spot... for nowcited by 842
- 2022TruFor: Leveraging all-round clues for trustworthy image forgery detection and localizationcited by 130
- 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 73
- 2023Shadows Don't Lie and Lines Can't Bend! Generative Models don't know Projective Geometry...for nowcited by 61
- 2023Online Detection of AI-Generated Imagescited by 55
- 2022Lighting (In)consistency of Paint by Textcited by 35
- 2022Perspective (In)consistency of Paint by Textcited by 34
- 2023Qualitative Failures of Image Generation Models and Their Application in Detecting Deepfakescited by 31
- 2023The Age of Synthetic Realities: Challenges and Opportunitiescited by 15
- 2024SynArtifact: Classifying and Alleviating Artifacts in Synthetic Images via Vision-Language Modelcited by 15
4 more
- 2024Characteristics and prevalence of fake social media profiles with AI-generated facescited by 14
- 2023GenAI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusionscited by 10
- 2023Finding AI-Generated Faces in the Wildcited by 8
- 2024Synthetic Image Generation in Cyber Influence Operations: An Emergent Threat?cited by 1
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.
- 2025LEGION: Learning to Ground and Explain for Synthetic Image Detectioncited by 65
- 2025Explainable Synthetic Image Detection through Diffusion Timestep Ensemblingcited by 7
- 2024Constructive Apraxia: An Unexpected Limit of Instructible Vision-Language Models and Analog for Human Cognitive Disorderscited by 0
- 2025LEGION: Learning to Ground and Explain for Synthetic Image Detection19 Mar 2025
- 2025Explainable Synthetic Image Detection through Diffusion Timestep Ensembling8 Mar 2025
- 2024Constructive Apraxia: An Unexpected Limit of Instructible Vision-Language Models and Analog for Human Cognitive Disorders17 Sep 2024
Similar, not cited
Close in wording, no citation either way
- 2025Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images
- 2024How to Distinguish AI-Generated Images from Authentic Photographs
- 2023GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image
- 2024Unveiling the Truth: Exploring Human Gaze Patterns in Fake Images
- 2025Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation
- 2025Detecting AI-Generated Images via Diffusion Snap-Back Reconstruction: A Forensic Approach
- 2025VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems
- 2026Repurposing Image Diffusion Models for Adversarial Synthetic Structured Data: A Case Study of Ground Truth Drift