Image manipulation detection algorithms are often trained to discriminate between images manipulated with particular Generative Models (GMs) and genuine/real images, yet generalize poorly to images manipulated with GMs unseen in the training. Conventional detection algorithms receive an input image passively. By contrast, we propose a proactive scheme to image manipulation detection. Our key enabling technique is to estimate a set of templates which when added onto the real image would lead to more accurate manipulation detection. That is, a template protected real image, and its manipulated version, is better discriminated compared to the original real image vs. its manipulated one. These templates are estimated using certain constraints based on the desired properties of templates. For image manipulation detection, our proposed approach outperforms the prior work by an average precision of 16% for CycleGAN and 32% for GauGAN. Our approach is generalizable to a variety of GMs showing an improvement over prior work by an average precision of 10% averaged across 12 GMs. Our code is available at

Passive vs. proactive image manipulation detection

Figure 1. • Classic passive schemes take an image as it is to discriminate a real image vs. its manipulated one created by a Generative Model (GM). In contrast, our proactive scheme performs encryption of the real image so that our detection module can better discriminate the encrypted real image vs. its manipulated counterpart.

The proposed approach

Figure 2. • Our proposed framework includes two stages: 1) selection and addition of templates; and 2) the recovery of the estimated template from encrypted real images and manipulated images using an encoder network. The GM is used in the inference mode. Both stages are trained in an end-to-end manner to output a set of templates. For inferences, the first stage is mandatory to encrypt the images. The second stage is used only when there is a need of image manipulation detection.

Template Visualisation

Figure 3. • Visualization of (a) a template set with the size of 3, (b) real images, (c) encrypted real images after adding template, (d) manipulated images output by a GM, (e) recovered template from (c), and (f) recovered template from (d). Each row corresponds to image manipulation by different GM (top: StarGAN, middle: CycleGAN, bottom: GauGAN). The template recovered from encrypted real images is more similar to the template set than the one from manipulated images. The addition of the template creates no visual difference between real and encrypted real images. We provide more examples of real images evaluated using our framework in the supplementary material.

Proactive Image Manipulation Detection Source Code

The source code can be downloaded from here


  • Proactive Image Manipulation Detection
    Vishal Asnani, Xi Yin, Tal Hassner, Sijia Liu, Xiaoming Liu
    In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2022), New Orleans, LA, Jun. 2022
    Bibtex | PDF | arXiv | Supplemental | Code
  • @inproceedings{ proactive-image-manipulation-detection,
      author = { Vishal Asnani and Xi Yin and Tal Hassner and Sijia Liu and Xiaoming Liu },
      title = { Proactive Image Manipulation Detection },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { New Orleans, LA },
      month = { June },
      year = { 2022 },