Papers Image Forgery Detection
“Image Forgery Detection” 태그가 달린 논문 47편 · 필터 해제
SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection
The rapid advancement of generative artificial intelligence is producing fake remote sensing imagery (RSI) that is increasingly difficult to detect, potentially leading to erroneous intelligence, fake news, and even cons…
Image Forgery DetectionSo-Fake: Benchmarking and Explaining Social Media Image Forgery Detection
Recent advances in AI-powered generative models have enabled the creation of increasingly realistic synthetic images, posing significant risks to information integrity and public trust on social media platforms. While ro…
BenchmarkingImage Forgery DetectionLearning Universal Features for Generalizable Image Forgery Localization
In recent years, advanced image editing and generation methods have rapidly evolved, making detecting and locating forged image content increasingly challenging. Most existing image forgery detection methods rely on iden…
Image Forgery DetectionDF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection
The deliberate manipulation of public opinion, especially through altered images, which are frequently disseminated through online social networks, poses a significant danger to society. To fight this issue on a technica…
Image Forgery DetectionDF-Net: The Digital Forensics Network for Image Forgery Detection
The orchestrated manipulation of public opinion, particularly through manipulated images, often spread via online social networks (OSN), has become a serious threat to society. In this paper we introduce the Digital Fore…
Image Forgery DetectionLanguage-guided Hierarchical Fine-grained Image Forgery Detection and Localization
Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging. To this end, …
AttributeImage Forgery DetectionRepresentation LearningForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization
Multimodal Large Language Models (MLLMs), such as GPT4o, have shown strong capabilities in visual reasoning and explanation generation. However, despite these strengths, they face significant challenges in the increasing…
Explanation GenerationImage Forgery DetectionInstruction FollowingLanguage Modeling+4FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models
The rapid development of generative AI is a double-edged sword, which not only facilitates content creation but also makes image manipulation easier and more difficult to detect. Although current image forgery detection …
Face SwappingImage Forgery DetectionImage ManipulationTAGDigital Image Forensics: A quantitative & qualitative comparison between State-of-the-art-AI and Traditional Techniques for detection and localization of image manipulations
With the rise of realistic AI-generated images and continuously advancing photo-editing software, it has become increasingly difficult to reliably distinguish between authentic and manipulated images. Using Digital Image…
Detecting Image ManipulationImage ForensicsImage Forgery DetectionImage Manipulation+4Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning
Three key challenges hinder the development of current deepfake video detection: (1) Temporal features can be complex and diverse: how can we identify general temporal artifacts to enhance model generalization? (2) Spati…
Face SwappingImage Forgery DetectionRecompression Based JPEG Tamper Detection and Localization Using Deep Neural Network Eliminating Compression Factor Dependency
In this work, we deal with the problem of re compression based image forgery detection, where some regions of an image are modified illegitimately, hence giving rise to presence of dual compression characteristics within…
Image ForensicsImage Forgery DetectionImage ManipulationDeep Image Restoration For Image Anti-Forensics
While image forensics is concerned with whether an image has been tampered with, image anti-forensics attempts to prevent image forensics methods from detecting tampered images. The competition between these two fields s…
Data AugmentationDeep LearningImage ForensicsImage Forgery Detection+3Deep Image Composition Meets Image Forgery
Image forgery is a topic that has been studied for many years. Before the breakthrough of deep learning, forged images were detected using handcrafted features that did not require training. These traditional methods fai…
Deep LearningImage Forgery DetectionImage ManipulationImage Manipulation DetectionFusion Transformer with Object Mask Guidance for Image Forgery Analysis
In this work, we introduce OMG-Fuser, a fusion transformer-based network designed to extract information from various forensic signals to enable robust image forgery detection and localization. Our approach can operate w…
Image ForensicsImage Forgery DetectionDiffForensics: Leveraging Diffusion Prior to Image Forgery Detection and Localization
As manipulating images may lead to misinterpretation of the visual content addressing the image forgery detection and localization (IFDL) problem has drawn serious public concerns. In this work we propose a simple as…
DecoderDenoisingImage Forgery DetectionMMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization
Recent image manipulation localization and detection techniques typically leverage forensic artifacts and traces that are produced by a noise-sensitive filter, such as SRM or Bayar convolution. In this paper, we showcase…
Detecting Image ManipulationImage ForensicsImage Forgery DetectionImage Manipulation+2Rethinking Image Forgery Detection via Soft Contrastive Learning and Unsupervised Clustering
Image forgery detection aims to detect and locate forged regions in an image. Most existing forgery detection algorithms formulate classification problems to classify pixels into forged or pristine. However, the definiti…
ClusteringContrastive LearningImage Forgery DetectionDiscrepancy-Guided Reconstruction Learning for Image Forgery Detection
In this paper, we propose a novel image forgery detection paradigm for boosting the model learning capacity on both forgery-sensitive and genuine compact visual patterns. Compared to the existing methods that only focus …
Image Forgery DetectionHierarchical Fine-Grained Image Forgery Detection and Localization
Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging. To this end, …
AttributeClassificationImage Forgery DetectionRepresentation LearningTruFor: Leveraging all-round clues for trustworthy image forgery detection and localization
In this paper we present TruFor, a forensic framework that can be applied to a large variety of image manipulation methods, from classic cheapfakes to more recent manipulations based on deep learning. We rely on the extr…
AllImage Forgery DetectionImage ManipulationImage Manipulation Detection+1