Electrical Engineering and Systems Science > Image and Video Processing
[Submitted on 8 Oct 2022 (v1), last revised 29 Oct 2022 (this version, v2)]
Title:Convolutional Neural Network-Based Image Watermarking using Discrete Wavelet Transform
View PDFAbstract:With the growing popularity of the Internet, digital images are used and transferred more frequently. Although this phenomenon facilitates easy access to information, it also creates security concerns and violates intellectual property rights by allowing illegal use, copying, and digital content theft. Using watermarks in digital images is one of the most common ways to maintain security. Watermarking is proving and declaring ownership of an image by adding a digital watermark to the original image. Watermarks can be either text or an image placed overtly or covertly in an image and are expected to be challenging to remove. This paper proposes a combination of convolutional neural networks (CNNs) and wavelet transforms to obtain a watermarking network for embedding and extracting watermarks. The network is independent of the host image resolution, can accept all kinds of watermarks, and has only 11 layers while keeping performance. Performance is measured by two terms; the similarity between the extracted watermark and the original one and the similarity between the host image and the watermarked one.
Submission history
From: Alireza Tavakoli [view email][v1] Sat, 8 Oct 2022 22:54:46 UTC (4,137 KB)
[v2] Sat, 29 Oct 2022 12:52:47 UTC (4,140 KB)
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