MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression

TL;DR

This paper introduces MBRS, a novel deep learning watermarking framework that significantly improves robustness against JPEG compression and other distortions by using a mini-batch training approach with real and simulated JPEGs, along with advanced feature learning techniques.

Contribution

The paper proposes a new end-to-end training architecture utilizing mini-batches of real and simulated JPEG compression to enhance robustness, along with novel network components like Squeeze-and-Excitation blocks and a message processor.

Findings

Achieves less than 0.01% bit error rate under JPEG Q=50

Maintains PSNR above 36 for encoded images

Demonstrates robustness against various distortions

Abstract

Based on the powerful feature extraction ability of deep learning architecture, recently, deep-learning based watermarking algorithms have been widely studied. The basic framework of such algorithm is the auto-encoder like end-to-end architecture with an encoder, a noise layer and a decoder. The key to guarantee robustness is the adversarial training with the differential noise layer. However, we found that none of the existing framework can well ensure the robustness against JPEG compression, which is non-differential but is an essential and important image processing operation. To address such limitations, we proposed a novel end-to-end training architecture, which utilizes Mini-Batch of Real and Simulated JPEG compression (MBRS) to enhance the JPEG robustness. Precisely, for different mini-batches, we randomly choose one of real JPEG, simulated JPEG and noise-free layer as the noise layer. Besides, we suggest to utilize the Squeeze-and-Excitation blocks which can learn better feature in embedding and extracting stage, and propose a "message processor" to expand the message in a more appreciate way. Meanwhile, to improve the robustness against crop attack, we propose an additive diffusion block into the network. The extensive experimental results have demonstrated the superior performance of the proposed scheme compared with the state-of-the-art algorithms. Under the JPEG compression with quality factor Q=50, our models achieve a bit error rate less than 0.01% for extracted messages, with PSNR larger than 36 for the encoded images, which shows the well-enhanced robustness against JPEG attack. Besides, under many other distortions such as Gaussian filter, crop, cropout and dropout, the proposed framework also obtains strong robustness. The code implemented by PyTorch \cite{2011torch7} is avaiable in https://github.com/jzyustc/MBRS.