# Generate, Segment and Refine: Towards Generic Manipulation Segmentation

arXiv: https://arxiv.org/abs/1811.09729 · HTML: https://mlsanity.com/paper/1811.09729/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2018.

## Built on by (most cited first, 30 of 33)

- [Hierarchical Fine-Grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2303.17111/) (2023)
- [MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection](https://mlsanity.com/paper/2112.08935/) (2021)
- [Image Manipulation Detection by Multi-View Multi-Scale Supervision](https://mlsanity.com/paper/2104.06832/) (2021)
- [IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer](https://mlsanity.com/paper/2307.14863/) (2023)
- [Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltales](https://mlsanity.com/paper/2107.05821/) (2021)
- [Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning](https://mlsanity.com/paper/2309.01246/) (2023)
- [Language-guided Hierarchical Fine-grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2410.23556/) (2024)
- [Exploring Multi-view Pixel Contrast for General and Robust Image Forgery Localization](https://mlsanity.com/paper/2406.13565/) (2024)
- [ForgeryGPT: A Multimodal LLM for Interpretable Image Forgery Detection and Localization](https://mlsanity.com/paper/2410.10238/) (2024)
- [Deep Video Inpainting Detection](https://mlsanity.com/paper/2101.11080/) (2021)
- [GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection](https://mlsanity.com/paper/2112.04298/) (2021)
- [Multi-spectral Class Center Network for Face Manipulation Detection and Localization](https://mlsanity.com/paper/2305.10794/) (2023)
- [Towards Effective Image Manipulation Detection with Proposal Contrastive Learning](https://mlsanity.com/paper/2210.08529/) (2022)
- [TBFormer: Two-Branch Transformer for Image Forgery Localization](https://mlsanity.com/paper/2302.13004/) (2023)
- [FrozenSeg: Harmonizing Frozen Foundation Models for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2409.03525/) (2024)
- [TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2308.05264/) (2023)
- [Noise and Edge Based Dual Branch Image Manipulation Detection](https://mlsanity.com/paper/2207.00724/) (2022)
- [Dense Feature Interaction Network for Image Inpainting Localization](https://mlsanity.com/paper/2408.02191/) (2024)
- [Inharmonious Region Localization by Magnifying Domain Discrepancy](https://mlsanity.com/paper/2209.15368/) (2022)
- [Context-Aware Weakly Supervised Image Manipulation Localization with SAM Refinement](https://mlsanity.com/paper/2503.20294/) (2025)
- [Learning Hierarchical Graph Representation for Image Manipulation Detection](https://mlsanity.com/paper/2201.05730/) (2022)
- [MSMG-Net: Multi-scale Multi-grained Supervised Metworks for Multi-task Image Manipulation Detection and Localization](https://mlsanity.com/paper/2211.03140/) (2022)
- [UVL2: A Unified Framework for Video Tampering Localization](https://mlsanity.com/paper/2309.16126/) (2023)
- [Video Inpainting Localization with Contrastive Learning](https://mlsanity.com/paper/2406.17628/) (2024)
- [A Noise and Edge extraction-based dual-branch method for Shallowfake and Deepfake Localization](https://mlsanity.com/paper/2409.00896/) (2024)
- [Inharmonious Region Localization via Recurrent Self-Reasoning](https://mlsanity.com/paper/2210.02036/) (2022)
- [Auto-Focus Contrastive Learning for Image Manipulation Detection](https://mlsanity.com/paper/2211.10922/) (2022)
- [Perceptual MAE for Image Manipulation Localization: A High-level Vision Learner Focusing on Low-level Features](https://mlsanity.com/paper/2310.06525/) (2023)
- [Mumpy: Multilateral Temporal-view Pyramid Transformer for Video Inpainting Detection](https://mlsanity.com/paper/2404.11054/) (2024)
- [Trusted Video Inpainting Localization via Deep Attentive Noise Learning](https://mlsanity.com/paper/2406.13576/) (2024)

## Abstract (arXiv)

Detecting manipulated images has become a significant emerging challenge. The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet. While the intent behind such manipulations varies widely, concerns on the spread of fake news and misinformation is growing. Current state of the art methods for detecting these manipulated images suffers from the lack of training data due to the laborious labeling process. We address this problem in this paper, for which we introduce a manipulated image generation process that creates true positives using currently available datasets. Drawing from traditional work on image blending, we propose a novel generator for creating such examples. In addition, we also propose to further create examples that force the algorithm to focus on boundary artifacts during training. Strong experimental results validate our proposal.