Self-Supervised Image Local Forgery Detection by JPEG Compression Trace
Authors: Xiuli Bi, Wuqing Yan, Bo Liu, Bin Xiao, Weisheng Li, Xinbo Gao
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that the proposed method can detect image local forgery on different datasets without re-training, and keep stable performance over various types of image local forgery. Extensive experiments show that the proposed method has a good ability to detect various local forgeries in JPEG images and can resist cropping attacks well. Experiments Dataset and Metric Experimental Dataset. Ablation Study. Comparison with the State-of-the-Art. |
| Researcher Affiliation | Academia | Xiuli Bi, Wuqing Yan, Bo Liu, Bin Xiao*, Weisheng Li, Xinbo Gao Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China {bixl, boliu, xiaobin, liws, gaoxb}@cqupt.edu.cn, s210201122@stu.cqupt.edu.cn |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. It describes the methods in narrative form and with figures. |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code available, nor does it include links to a code repository. |
| Open Datasets | Yes | We only used 200 TIFF-formated images randomly selected from the ALASKA (Ruiz et al. 2021) dataset. |
| Dataset Splits | No | The paper mentions cropping images into patches for training ("we cropped 200 TIFF images into 12800 48x48 patches as the training set") but does not specify a separate validation split or how it was handled. |
| Hardware Specification | Yes | Our proposed method was implemented by Tensorflow and trained on NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions "Tensorflow" as the implementation framework but does not provide specific version numbers for it or any other key software dependencies. |
| Experiment Setup | Yes | The batch size was set to 128, and the patches within a batch are randomly compressed with a quality factor QF [50, 100]. The Adam optimizer was used with the learning rate of 0.001, and the λ in Eq. 8 was set to 0.1. In self-supervised training, each batch, sized 200, is divided into 50 groups (N=50), and each group is compressed with a different quality factor in [50, 100]. The Adam optimizer was used with a learning rate of 0.0001. |