Papers Image Manipulation Localization
“Image Manipulation Localization” 태그가 달린 논문 54편 · 필터 해제
GAP-SAM: A Global Artifact Prior for Generalizable AI-Generated Image Manipulation Localization
AI-generated image manipulation localization identifies edited pixels, but its OOD performance lags behind image-level detection partly because pixel supervision entangles forensic evidence with dataset-specific mask geo…
Image Manipulation LocalizationZero-Shot Color Image Manipulation Localization via Noise Residual Artifact Pattern Analysis
Digital cameras embed device-specific artifacts into every acquired image through demosaicing, in-camera post-processing, and lossy compression. These traces constitute a forensic signal that can be exploited to assess i…
Image Manipulation LocalizationForensicsTok: Forensics-Guided Tokenized Modeling for Image Tampering Localization
Multi-modal Large Language Models (MLLMs) offer powerful reasoning for forensic tasks, yet existing approaches utilizing exogenous segmentation decoders often suffer from suboptimal localization. The reliance on stitched…
Image Manipulation LocalizationImpostor: An Agent-Curated Benchmark for Realistic AIGC Manipulation Localization
Recent advances in generative image editing have improved the realism and controllability of localized image manipulation, raising new challenges for image manipulation detection and localization (IMDL). However, existin…
Image Manipulation LocalizationImage Manipulation DetectionImage EditingSIGMA: Semantic-Difference Instruction-Grounding Mask Annotator for Text-Driven Image Manipulation Localization
Text-driven image editing has advanced rapidly, but reliably localizing these manipulations requires image manipulation localization (IML) models trained on large pixel-annotated datasets, and there is still no low-cost …
Image Manipulation LocalizationImage EditingMulti-axis Analysis of Image Manipulation Localization
Advanced image editing software enables easy creation of highly convincing image manipulations, which has been made even more accessible in recent years due to advances in generative AI. Manipulated images, while often h…
Image Manipulation LocalizationImage Manipulation DetectionImage EditingTowards Generalized Image Manipulation Localization via Score-based Model
With the rapid evolution of synthetic media, Image Manipulation Localization (IML) has emerged as a critical component in multimedia forensics for ensuring the integrity of digital content. However, generalization remain…
Image Manipulation LocalizationThe Courtroom Trial of Pixels: Robust Image Manipulation Localization via Adversarial Evidence and Reinforcement Learning Judgment
Although some existing image manipulation localization (IML) methods incorporate authenticity-related supervision, this information is typically utilized merely as an auxiliary training signal to enhance the model's sens…
Image Manipulation LocalizationReinforcement LearningBridging the Micro--Macro Gap: Frequency-Aware Semantic Alignment for Image Manipulation Localization
As generative image editing advances, image manipulation localization (IML) must handle both traditional manipulations with conspicuous forensic artifacts and diffusion-generated edits that appear locally realistic. Exis…
Image Manipulation LocalizationImage EditingSemantic Manipulation Localization
Image Manipulation Localization (IML) aims to identify edited regions in an image. However, with the increasing use of modern image editing and generative models, many manipulations no longer exhibit obvious low-level ar…
Image Manipulation LocalizationArtifact DetectionImage EditingOff-the-shelf Vision Models Benefit Image Manipulation Localization
Image manipulation localization (IML) and general vision tasks are typically treated as two separate research directions due to the fundamental differences between manipulation-specific and semantic features. In this pap…
Image Manipulation LocalizationImage GenerationRecoverMark: Robust Watermarking for Localization and Recovery of Manipulated Faces
The proliferation of AI-generated content has facilitated sophisticated face manipulation, severely undermining visual integrity and posing unprecedented challenges to intellectual property. In response, a common proacti…
Image Manipulation LocalizationSAPL: Semantic-Agnostic Prompt Learning in CLIP for Weakly Supervised Image Manipulation Localization
Malicious image manipulation threatens public safety and requires efficient localization methods. Existing approaches depend on costly pixel-level annotations which make training expensive. Existing weakly supervised met…
Image Manipulation LocalizationContrastive LearningShallow- and Deep-fake Image Manipulation Localization Using Vision Mamba and Guided Graph Neural Network
Image manipulation localization is a critical research task, given that forged images may have a significant societal impact of various aspects. Such image manipulations can be produced using traditional image editing to…
Image Manipulation LocalizationGraph Neural NetworkImage EditingDEAL-300K: Diffusion-based Editing Area Localization with a 300K-Scale Dataset and Frequency-Prompted Baseline
Diffusion-based image editing has made semantic level image manipulation easy for general users, but it also enables realistic local forgeries that are hard to localize. Existing benchmarks mainly focus on the binary det…
Image Manipulation LocalizationChange DetectionImage EditingFrom Passive Perception to Active Memory: A Weakly Supervised Image Manipulation Localization Framework Driven by Coarse-Grained Annotations
Image manipulation localization (IML) faces a fundamental trade-off between minimizing annotation cost and achieving fine-grained localization accuracy. Existing fully-supervised IML methods depend heavily on dense pixel…
Image Manipulation LocalizationKnowledge DistillationTraining-Free In-Context Forensic Chain for Image Manipulation Detection and Localization
Advances in image tampering pose serious security threats, underscoring the need for effective image manipulation localization (IML). While supervised IML achieves strong performance, it depends on costly pixel-level ann…
Image Manipulation LocalizationImage Manipulation DetectionRevisiting Image Manipulation Localization under Realistic Manipulation Scenarios
With the large models easing the labor-intensive manipulation process, image manipulations in today's real scenarios often entail a complex manipulation process, comprising a series of editing operations to create a dece…
Image Manipulation LocalizationEfficientIML: Efficient High-Resolution Image Manipulation Localization
With imaging devices delivering ever-higher resolutions and the emerging diffusion-based forgery methods, current detectors trained only on traditional datasets (with splicing, copy-moving and object removal forgeries) l…
Image Manipulation LocalizationWebly-Supervised Image Manipulation Localization via Category-Aware Auto-Annotation
Images manipulated by image editing tools can mislead viewers and pose significant risks to social security. However, accurately localizing manipulated image regions remains challenging due to the severe scarcity of high…
Image Manipulation LocalizationImage Editing