Papers Fake Image Detection
“Fake Image Detection” 태그가 달린 논문 37편 · 필터 해제
ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization
The field of Fake Image Detection and Localization (FIDL) is highly fragmented, encompassing four domains: deepfake detection (Deepfake), image manipulation detection and localization (IMDL), artificial intelligence-gene…
AllDeepFake DetectionFace SwappingFake Image Detection+3Advance Fake Video Detection via Vision Transformers
Recent advancements in AI-based multimedia generation have enabled the creation of hyper-realistic images and videos, raising concerns about their potential use in spreading misinformation. The widespread accessibility o…
Fake Image DetectionFew-Shot LearningMisinformationTowards Explainable Fake Image Detection with Multi-Modal Large Language Models
Progress in image generation raises significant public security concerns. We argue that fake image detection should not operate as a "black box". Instead, an ideal approach must ensure both strong generalization and tran…
Fake Image DetectionImage GenerationTruthLens:A Training-Free Paradigm for DeepFake Detection
The proliferation of synthetic images generated by advanced AI models poses significant challenges in identifying and understanding manipulated visual content. Current fake image detection methods predominantly rely on b…
Binary ClassificationDeepFake DetectionFace SwappingFake Image Detection+3PDA: Generalizable Detection of AI-Generated Images via Post-hoc Distribution Alignment
The rapid advancement of generative models has led to the proliferation of highly realistic AI-generated images, posing significant challenges for detection methods to generalize across diverse and evolving generative te…
Fake Image DetectionStay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection
Detecting AI generated images is a challenging yet essential task. A primary difficulty arises from the detectors tendency to rely on spurious patterns, such as compression artifacts, which can influence its decisions. T…
Fake Image DetectionVisual Counter Turing Test (VCT^2): Discovering the Challenges for AI-Generated Image Detection and Introducing Visual AI Index (V_AI)
The proliferation of AI techniques for image generation, coupled with their increasing accessibility, has raised significant concerns about the potential misuse of these images to spread misinformation. Recent AI-generat…
Fake Image DetectionImage GenerationMisinformationDetecting AutoEncoder is Enough to Catch LDM Generated Images
In recent years, diffusion models have become one of the main methods for generating images. However, detecting images generated by these models remains a challenging task. This paper proposes a novel method for detectin…
Fake Image DetectionOn the Effectiveness of Dataset Alignment for Fake Image Detection
As latent diffusion models (LDMs) democratize image generation capabilities, there is a growing need to detect fake images. A good detector should focus on the generative models fingerprints while ignoring image properti…
DenoisingFake Image DetectionImage GenerationFIDAVL: Fake Image Detection and Attribution using Vision-Language Model
We introduce FIDAVL: Fake Image Detection and Attribution using a Vision-Language Model. FIDAVL is a novel and efficient mul-titask approach inspired by the synergies between vision and language processing. Leveraging th…
AttributeFake Image DetectionLanguage ModelingLanguage Modelling+1FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Models
The ability to distinguish whether an image is generated by artificial intelligence (AI) is a crucial ingredient in human intelligence, usually accompanied by a complex and dialectical forensic and reasoning process. How…
Binary ClassificationFake Image DetectionQuestion AnsweringASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale
Generative image models have emerged as a promising technology to produce realistic images. Despite potential benefits, concerns grow about its misuse, particularly in generating deceptive images that could raise signifi…
Fake Image DetectionDetecting Origin Attribution for Text-to-Image Diffusion Models
Modern text-to-image (T2I) diffusion models can generate images with remarkable realism and creativity. These advancements have sparked research in fake image detection and attribution, yet prior studies have not fully e…
Fake Image DetectionImage AttributionImage GenerationTransfer Learning of Real Image Features with Soft Contrastive Loss for Fake Image Detection
In the last few years, the artifact patterns in fake images synthesized by different generative models have been inconsistent, leading to the failure of previous research that relied on spotting subtle differences betwee…
Contrastive LearningFake Image DetectionTransfer LearningUnveiling the Truth: Exploring Human Gaze Patterns in Fake Images
Creating high-quality and realistic images is now possible thanks to the impressive advancements in image generation. A description in natural language of your desired output is all you need to obtain breathtaking result…
Fake Image DetectionImage GenerationMisinformationX-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection
Generative adversarial networks (GANs) have remarkably advanced in diverse domains, especially image generation and editing. However, the misuse of GANs for generating deceptive images, such as face replacement, raises s…
Fake Image DetectionImage GenerationTransfer LearningParents and Children: Distinguishing Multimodal DeepFakes from Natural Images
Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. While these models have numerous benefits across various sectors, they have also raised…
DeepFake DetectionFace SwappingFake Image Detectionimage-classification+1Information-containing Adversarial Perturbation for Combating Facial Manipulation Systems
With the development of deep learning technology, the facial manipulation system has become powerful and easy to use. Such systems can modify the attributes of the given facial images, such as hair color, gender, and age…
Fake Image DetectionLearning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection
Recently, there has been a significant advancement in image generation technology, known as GAN. It can easily generate realistic fake images, leading to an increased risk of abuse. However, most image detectors suff…
Fake Image DetectionImage GenerationDE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models
Text-to-image generation models that generate images based on prompt descriptions have attracted an increasing amount of attention during the past few months. Despite their encouraging performance, these models raise con…
AttributeFake Image DetectionImage GenerationText to Image Generation+1