Image Manipulation Detection
26 papers with code • 5 benchmarks • 2 datasets
The task of detecting images or image parts that have been tampered or manipulated (sometimes also referred to as doctored). This typically encompasses image splicing, copy-move, or image inpainting.
Benchmarks
These leaderboards are used to track progress in Image Manipulation Detection
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Libraries
Use these libraries to find Image Manipulation Detection models and implementations
Casia V1+
Latest papers with no code
Exploring Saliency Bias in Manipulation Detection
The social media-fuelled explosion of fake news and misinformation supported by tampered images has led to growth in the development of models and datasets for image manipulation detection.
TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization
The evaluation datasets and metrics for image manipulation detection and localization (IMDL) research have been standardized.
RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection
The emergence of different sensors (Near-Infrared, Depth, etc.)
On the Effectiveness of Image Manipulation Detection in the Age of Social Media
To this end, we introduce an anomaly enhancement loss that, when used with a residual architecture, improves the performance of different detection algorithms with a minimal introduction of false positives on the non-manipulated data.
Uncertainty-guided Learning for Improving Image Manipulation Detection
Image manipulation detection (IMD) is of vital importance as faking images and spreading misinformation can be malicious and harm our daily life.
SAFL-Net: Semantic-Agnostic Feature Learning Network with Auxiliary Plugins for Image Manipulation Detection
Since image editing methods in real world scenarios cannot be exhausted, generalization is a core challenge for image manipulation detection, which could be severely weakened by semantically related features.
Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning
To improve the generalization ability, we propose weakly-supervised self-consistency learning (WSCL) to leverage the weakly annotated images.
TruFor: 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.
Auto-Focus Contrastive Learning for Image Manipulation Detection
However, we argue that those models achieve sub-optimal detection performance as it tends to: 1) distinguish the manipulation traces from a lot of noisy information within the entire image, and 2) ignore the trace relations among the pixels of each manipulated region and its surroundings.
MSMG-Net: Multi-scale Multi-grained Supervised Metworks for Multi-task Image Manipulation Detection and Localization
Then the multi-grained feature learning is utilized to perceive object-level semantics relation of multi-scale features by introducing the shunted self-attention.
