How to Distinguish AI-Generated Images from Authentic Photographs
- URL: http://arxiv.org/abs/2406.08651v1
- Date: Wed, 12 Jun 2024 21:23:27 GMT
- Title: How to Distinguish AI-Generated Images from Authentic Photographs
- Authors: Negar Kamali, Karyn Nakamura, Angelos Chatzimparmpas, Jessica Hullman, Matthew Groh,
- Abstract summary: Guide reveals five categories of artifacts and implausibilities that often appear in AI-generated images.
We generated 138 images with diffusion models, curated 9 images from social media, and curated 42 real photographs.
By drawing attention to these kinds of artifacts and implausibilities, we aim to better equip people to distinguish AI-generated images from real photographs.
- Score: 13.878791907839691
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The high level of photorealism in state-of-the-art diffusion models like Midjourney, Stable Diffusion, and Firefly makes it difficult for untrained humans to distinguish between real photographs and AI-generated images. To address this problem, we designed a guide to help readers develop a more critical eye toward identifying artifacts, inconsistencies, and implausibilities that often appear in AI-generated images. The guide is organized into five categories of artifacts and implausibilities: anatomical, stylistic, functional, violations of physics, and sociocultural. For this guide, we generated 138 images with diffusion models, curated 9 images from social media, and curated 42 real photographs. These images showcase the kinds of cues that prompt suspicion towards the possibility an image is AI-generated and why it is often difficult to draw conclusions about an image's provenance without any context beyond the pixels in an image. Human-perceptible artifacts are not always present in AI-generated images, but this guide reveals artifacts and implausibilities that often emerge. By drawing attention to these kinds of artifacts and implausibilities, we aim to better equip people to distinguish AI-generated images from real photographs in the future.
Related papers
- Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images [13.097947037585671]
Given the challenge to public trust in media posed by photorealistic AI-generated images, we conducted a large-scale experiment measuring human detection accuracy.
We find that scene complexity, artifact types within an image, display time of an image, and human curation of AI-generated images all play significant roles in how accurately people distinguish real from AI-generated images.
arXiv Detail & Related papers (2025-02-17T16:28:15Z) - Self-Supervised Learning for Detecting AI-Generated Faces as Anomalies [58.11545090128854]
We describe an anomaly detection method for AI-generated faces by leveraging self-supervised learning of camera-intrinsic and face-specific features purely from photographic face images.
The success of our method lies in designing a pretext task that trains a feature extractor to rank four ordinal exchangeable image file format (EXIF) tags and classify artificially manipulated face images.
arXiv Detail & Related papers (2025-01-04T06:23:24Z) - Knowledge-Guided Prompt Learning for Deepfake Facial Image Detection [54.26588902144298]
We propose a knowledge-guided prompt learning method for deepfake facial image detection.
Specifically, we retrieve forgery-related prompts from large language models as expert knowledge to guide the optimization of learnable prompts.
Our proposed approach notably outperforms state-of-the-art methods.
arXiv Detail & Related papers (2025-01-01T02:18:18Z) - Detecting Discrepancies Between AI-Generated and Natural Images Using Uncertainty [91.64626435585643]
We propose a novel approach for detecting AI-generated images by leveraging predictive uncertainty to mitigate misuse and associated risks.
The motivation arises from the fundamental assumption regarding the distributional discrepancy between natural and AI-generated images.
We propose to leverage large-scale pre-trained models to calculate the uncertainty as the score for detecting AI-generated images.
arXiv Detail & Related papers (2024-12-08T11:32:25Z) - Crafting Synthetic Realities: Examining Visual Realism and Misinformation Potential of Photorealistic AI-Generated Images [6.308018793111589]
This study unpacks AI photorealism of AIGIs from four key dimensions, content, human, aesthetic, and production features.
photorealistic AIGIs often depict human figures, especially celebrities and politicians, with a high degree of surrealism and aesthetic professionalism.
arXiv Detail & Related papers (2024-09-26T02:46:43Z) - Synthetic Photography Detection: A Visual Guidance for Identifying Synthetic Images Created by AI [0.0]
Synthetic photographs may be used maliciously by a broad range of threat actors.
We show that visible artifacts in generated images 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.
arXiv Detail & Related papers (2024-08-12T08:58:23Z) - Development of a Dual-Input Neural Model for Detecting AI-Generated Imagery [0.0]
It is important to develop tools that are able to detect AI-generated images.
This paper proposes a dual-branch neural network architecture that takes both images and their Fourier frequency decomposition as inputs.
Our proposed model achieves an accuracy of 94% on the CIFAKE dataset, which significantly outperforms classic ML methods and CNNs.
arXiv Detail & Related papers (2024-06-19T16:42:04Z) - The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking [47.08666835021915]
We present a systematic attempt at understanding and detecting AI-generated images (AI-art) in adversarial scenarios.
The dataset, named ARIA, contains over 140K images in five categories: artworks (painting), social media images, news photos, disaster scenes, and anime pictures.
arXiv Detail & Related papers (2024-04-22T21:00:13Z) - Organic or Diffused: Can We Distinguish Human Art from AI-generated Images? [24.417027069545117]
Distinguishing AI generated images from human art is a challenging problem.
A failure to address this problem allows bad actors to defraud individuals paying a premium for human art and companies whose stated policies forbid AI imagery.
We curate real human art across 7 styles, generate matching images from 5 generative models, and apply 8 detectors.
arXiv Detail & Related papers (2024-02-05T17:25:04Z) - Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images [67.18010640829682]
We show that AI-generated images introduce an invisible relevance bias to text-image retrieval models.
The inclusion of AI-generated images in the training data of the retrieval models exacerbates the invisible relevance bias.
We propose an effective training method aimed at alleviating the invisible relevance bias.
arXiv Detail & Related papers (2023-11-23T16:22:58Z) - Seeing is not always believing: Benchmarking Human and Model Perception
of AI-Generated Images [66.20578637253831]
There is a growing concern that the advancement of artificial intelligence (AI) technology may produce fake photos.
This study aims to comprehensively evaluate agents for distinguishing state-of-the-art AI-generated visual content.
arXiv Detail & Related papers (2023-04-25T17:51:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.