Papers Image Manipulation Localization
“Image Manipulation Localization” 태그가 달린 논문 54편 · 필터 해제
Propose and Rectify: A Forensics-Driven MLLM Framework for Image Manipulation Localization
The increasing sophistication of image manipulation techniques demands robust forensic solutions that can both reliably detect alterations and precisely localize tampered regions. Recent Multimodal Large Language Models …
Image Manipulation LocalizationMultimodal ReasoningBridging Semantic Logic Gaps: A Cognition Inspired Multimodal Boundary Preserving Network for Image Manipulation Localization
The existing image manipulation localization (IML) models mainly relies on visual cues, but ignores the semantic logical relationships between content features. In fact, the content semantics conveyed by real images ofte…
Image Manipulation LocalizationUGD-IML: A Unified Generative Diffusion-based Framework for Constrained and Unconstrained Image Manipulation Localization
In the digital age, advanced image editing tools pose a serious threat to the integrity of visual content, making image forgery detection and localization a key research focus. Most existing Image Manipulation Localizati…
Image Manipulation LocalizationImage EditingBeyond Fully Supervised Pixel Annotations: Scribble-Driven Weakly-Supervised Framework for Image Manipulation Localization
Deep learning-based image manipulation localization (IML) methods have achieved remarkable performance in recent years, but typically rely on large-scale pixel-level annotated datasets. To address the challenge of acquir…
Image ManipulationImage Manipulation LocalizationForensicHub: 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+3Context-Aware Weakly Supervised Image Manipulation Localization with SAM Refinement
Malicious image manipulation poses societal risks, increasing the importance of effective image manipulation detection methods. Recent approaches in image manipulation detection have largely been driven by fully supervis…
Image ManipulationImage Manipulation DetectionImage Manipulation LocalizationCan We Get Rid of Handcrafted Feature Extractors? SparseViT: Nonsemantics-Centered, Parameter-Efficient Image Manipulation Localization through Spare-Coding Transformer
Non-semantic features or semantic-agnostic features, which are irrelevant to image context but sensitive to image manipulations, are recognized as evidential to Image Manipulation Localization (IML). Since manual labels …
Image ManipulationImage Manipulation LocalizationLearning Semantic RepresentationsMesoscopic Insights: Orchestrating Multi-scale & Hybrid Architecture for Image Manipulation Localization
The mesoscopic level serves as a bridge between the macroscopic and microscopic worlds, addressing gaps overlooked by both. Image manipulation localization (IML), a crucial technique to pursue truth from fake images, has…
Image ManipulationImage Manipulation LocalizationOmni-IML: Towards Unified Image Manipulation Localization
Existing Image Manipulation Localization (IML) methods mostly rely heavily on task-specific designs, making them perform well only on the target IML task, while joint training on multiple IML tasks causes significant per…
DecoderDescriptiveImage ForensicsImage Manipulation+1ForgeryTTT: Zero-Shot Image Manipulation Localization with Test-Time Training
Social media is increasingly plagued by realistic fake images, making it hard to trust content. Previous algorithms to detect these fakes often fail in new, real-world scenarios because they are trained on specific datas…
Image ManipulationImage Manipulation LocalizationA Noise and Edge extraction-based dual-branch method for Shallowfake and Deepfake Localization
The trustworthiness of multimedia is being increasingly evaluated by advanced Image Manipulation Localization (IML) techniques, resulting in the emergence of the IML field. An effective manipulation model necessitates th…
Face SwappingImage ManipulationImage Manipulation LocalizationDigital 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+4M^3:Manipulation Mask Manufacturer for Arbitrary-Scale Super-Resolution Mask
In the field of image manipulation localization (IML), the small quantity and poor quality of existing datasets have always been major issues. A dataset containing various types of manipulations will greatly help improve…
Change DetectionImage ForensicsImage ManipulationImage Manipulation Localization+1IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization
A comprehensive benchmark is yet to be established in the Image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermi…
GPUImage ManipulationImage Manipulation DetectionImage Manipulation LocalizationPROMPT-IML: Image Manipulation Localization with Pre-trained Foundation Models Through Prompt Tuning
Deceptive images can be shared in seconds with social networking services, posing substantial risks. Tampering traces, such as boundary artifacts and high-frequency information, have been significantly emphasized by mass…
Image ManipulationImage Manipulation LocalizationTowards Modern Image Manipulation Localization: A Large-Scale Dataset and Novel Methods
In recent years image manipulation localization has attracted increasing attention due to its pivotal role in ensuring social media security. However effectively identifying forged regions remains an open challenge. …
Image ManipulationImage Manipulation LocalizationMMFusion: 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+2Perceptual MAE for Image Manipulation Localization: A High-level Vision Learner Focusing on Low-level Features
Nowadays, multimedia forensics faces unprecedented challenges due to the rapid advancement of multimedia generation technology thereby making Image Manipulation Localization (IML) crucial in the pursuit of truth. The key…
Image ManipulationImage Manipulation LocalizationPixel-Inconsistency Modeling for Image Manipulation Localization
Digital image forensics plays a crucial role in image authentication and manipulation localization. Despite the progress powered by deep neural networks, existing forgery localization methodologies exhibit limitations wh…
Data AugmentationDemosaickingImage ForensicsImage Manipulation+1Pre-training-free Image Manipulation Localization through Non-Mutually Exclusive Contrastive Learning
Deep Image Manipulation Localization (IML) models suffer from training data insufficiency and thus heavily rely on pre-training. We argue that contrastive learning is more suitable to tackle the data insufficiency proble…
Contrastive LearningImage ManipulationImage Manipulation Localization