# Weakly-supervised Localization of Manipulated Image Regions Using Multi-resolution Learned Features

arXiv: https://arxiv.org/abs/2505.23586 · HTML: https://mlsanity.com/paper/2505.23586/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 7 of 7)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Hierarchical Fine-Grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2303.17111/) (2023)
- [Weakly-supervised deepfake localization in diffusion-generated images](https://mlsanity.com/paper/2311.04584/) (2023)
- [Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning](https://mlsanity.com/paper/2309.01246/) (2023)
- [DeepLab2: A TensorFlow Library for Deep Labeling](https://mlsanity.com/paper/2106.09748/) (2021)
- [Copy Move Source-Target Disambiguation through Multi-Branch CNNs](https://mlsanity.com/paper/1912.12640/) (2019)
- [Image Segmentation Keras : Implementation of Segnet, FCN, UNet, PSPNet and other models in Keras](https://mlsanity.com/paper/2307.13215/) (2023)

## Abstract (arXiv)

The explosive growth of digital images and the widespread availability of image editing tools have made image manipulation detection an increasingly critical challenge. Current deep learning-based manipulation detection methods excel in achieving high image-level classification accuracy, they often fall short in terms of interpretability and localization of manipulated regions. Additionally, the absence of pixel-wise annotations in real-world scenarios limits the existing fully-supervised manipulation localization techniques. To address these challenges, we propose a novel weakly-supervised approach that integrates activation maps generated by image-level manipulation detection networks with segmentation maps from pre-trained models. Specifically, we build on our previous image-level work named WCBnet to produce multi-view feature maps which are subsequently fused for coarse localization. These coarse maps are then refined using detailed segmented regional information provided by pre-trained segmentation models (such as DeepLab, SegmentAnything and PSPnet), with Bayesian inference employed to enhance the manipulation localization. Experimental results demonstrate the effectiveness of our approach, highlighting the feasibility to localize image manipulations without relying on pixel-level labels.