Abstract:

Some important documents such as contracts,certificates and notifications are often stored and disseminated in a digital format.However,due to the inclusion of key text information,such images are often easily illegally tampered with and used,causing serious social impact and harm.Meanwhile,taking personal privacy and security into account,people also tend to remove sensitive information from these digital documents.Malicious tampering and desensitization can both introduce extra traces to the original images,but there are differences in motivation and operations.Therefore,it is necessary to differentiate them to locate the tamper areas more accurately.To address this issue,we propose a convolutional encoder-decoder network,which has multi-level features of the encoder through U-Net connection,effectively learning tampering and desensitization traces.At the same time,several Squeeze-and-Excitation attention mechanism modules are introduced in the decoder to suppress image content and focus on weaker operation traces,to improve the detection ability of the network.To effectively assist network training,we build a document image forensics dataset containing common tampering and desensitization operations.Experimental results show that our model performs effectively both on this dataset and on the public tamper datasets,and outperforms comparison algorithms.At the same time,the proposed method is robust to several common post-processing operations.

Key words: document image, forgery localization, desensitization localization, U-Net, squeeze-and-excitation attention mechanism