# Learning to Immunize Images for Tamper Localization and Self-Recovery

arXiv: https://arxiv.org/abs/2210.15902 · HTML: https://mlsanity.com/paper/2210.15902/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 68 of its 144 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 30 of 68)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)

## Built on by (most cited first, 19 of 19)

- [FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models](https://mlsanity.com/paper/2410.02761/) (2024)
- [EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection](https://mlsanity.com/paper/2312.08883/) (2023)
- [OmniGuard: Hybrid Manipulation Localization via Augmented Versatile Deep Image Watermarking](https://mlsanity.com/paper/2412.01615/) (2024)
- [Pixel-Inconsistency Modeling for Image Manipulation Localization](https://mlsanity.com/paper/2310.00234/) (2023)
- [V2A-Mark: Versatile Deep Visual-Audio Watermarking for Manipulation Localization and Copyright Protection](https://mlsanity.com/paper/2404.16824/) (2024)
- [GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization](https://mlsanity.com/paper/2406.16531/) (2024)
- [PROMPT-IML: Image Manipulation Localization with Pre-trained Foundation Models Through Prompt Tuning](https://mlsanity.com/paper/2401.00653/) (2024)
- [Webly-Supervised Image Manipulation Localization via Category-Aware Auto-Annotation](https://mlsanity.com/paper/2508.20987/) (2025)
- [Towards Transferable Defense Against Malicious Image Edits](https://mlsanity.com/paper/2512.14341/) (2025)
- [DFREC: DeepFake Identity Recovery Based on Identity-aware Masked Autoencoder](https://mlsanity.com/paper/2412.07260/) (2024)
- [CAMME: Adaptive Deepfake Image Detection with Multi-Modal Cross-Attention](https://mlsanity.com/paper/2505.18035/) (2025)
- [RecoverMark: Robust Watermarking for Localization and Recovery of Manipulated Faces](https://mlsanity.com/paper/2602.20618/) (2026)
- [Detecting Text Manipulation in Images using Vision Language Models](https://mlsanity.com/paper/2509.10278/) (2025)
- [Training-Free In-Context Forensic Chain for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2510.10111/) (2025)
- [Creating Blank Canvas Against AI-enabled Image Forgery](https://mlsanity.com/paper/2511.22237/) (2025)
- [AI Security in the Foundation Model Era: A Comprehensive Survey from a Unified Perspective](https://mlsanity.com/paper/2603.24857/) (2026)
- [Addressing Image Authenticity When Cameras Use Generative AI](https://mlsanity.com/paper/2604.21879/) (2026)
- [Robust Image Self-Recovery against Tampering using Watermark Generation with Pixel Shuffling](https://mlsanity.com/paper/2511.22936/) (2025)
- [High-Fidelity Face Content Recovery via Tamper-Resilient Versatile Watermarking](https://mlsanity.com/paper/2603.23940/) (2026)

## Abstract (arXiv)

Digital images are vulnerable to nefarious tampering attacks such as content addition or removal that severely alter the original meaning. It is somehow like a person without protection that is open to various kinds of viruses. Image immunization (Imuge) is a technology of protecting the images by introducing trivial perturbation, so that the protected images are immune to the viruses in that the tampered contents can be auto-recovered. This paper presents Imuge+, an enhanced scheme for image immunization. By observing the invertible relationship between image immunization and the corresponding self-recovery, we employ an invertible neural network to jointly learn image immunization and recovery respectively in the forward and backward pass. We also introduce an efficient attack layer that involves both malicious tamper and benign image post-processing, where a novel distillation-based JPEG simulator is proposed for improved JPEG robustness. Our method achieves promising results in real-world tests where experiments show accurate tamper localization as well as high-fidelity content recovery. Additionally, we show superior performance on tamper localization compared to state-of-the-art schemes based on passive forensics.