Version 1
: Received: 9 February 2024 / Approved: 12 February 2024 / Online: 12 February 2024 (13:10:24 CET)
Version 2
: Received: 27 March 2024 / Approved: 27 March 2024 / Online: 28 March 2024 (08:30:32 CET)
How to cite:
Yamauchi, S.; Kawamura, M. A Neural-Network-Based Watermarking Method Approximating JPEG Quantization. Preprints2024, 2024020657. https://doi.org/10.20944/preprints202402.0657.v2
Yamauchi, S.; Kawamura, M. A Neural-Network-Based Watermarking Method Approximating JPEG Quantization. Preprints 2024, 2024020657. https://doi.org/10.20944/preprints202402.0657.v2
Yamauchi, S.; Kawamura, M. A Neural-Network-Based Watermarking Method Approximating JPEG Quantization. Preprints2024, 2024020657. https://doi.org/10.20944/preprints202402.0657.v2
APA Style
Yamauchi, S., & Kawamura, M. (2024). A Neural-Network-Based Watermarking Method Approximating JPEG Quantization. Preprints. https://doi.org/10.20944/preprints202402.0657.v2
Chicago/Turabian Style
Yamauchi, S. and Masaki Kawamura. 2024 "A Neural-Network-Based Watermarking Method Approximating JPEG Quantization" Preprints. https://doi.org/10.20944/preprints202402.0657.v2
Abstract
We propose a neural-network-based watermarking method that introduces the quantized activation function that approximates the quantization of JPEG compression. Many neural-network-based watermarking methods have been proposed. Conventional methods have acquired robustness against various attacks by introducing an attack simulation layer between the embedding network and the extraction network. The quantization process of JPEG compression was replaced by the noise addition process in the attack layer of the conventional methods. In this paper, we propose a quantized activation function that can simulate the JPEG quantization standard as it is in order to improve the robustness against the JPEG compression. Our quantized activation function consists of several hyperbolic tangent functions and is applied as an activation function for neural networks. Our network was introduced in the attack layer of ReDMark proposed by Ahmadi et al. to compare it with their method. That is, the embedding and extraction networks had the same structure. We compared the usual JPEG compressed images and the images applying the quantized activation function. The results showed that a network with quantized activation functions can approximate JPEG compression with high accuracy. We also compared the bit error rate (BER) of estimated watermarks generated by our network with those generated by ReDMark. We found that our network was able to produce estimated watermarks with lower BERs than those of ReDMark. Therefore, our network outperformed the conventional method with respect to image quality and BER.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
