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.
翻译:随着互联网的日益普及,数字图像的使用和传输更加频繁。虽然这一现象便于方便获取信息,但它也造成了安全关切,并允许非法使用、复制和数字内容盗窃,从而侵犯了知识产权。在数字图像中使用水印是维护安全的最常见方法之一。水标记正在通过在原始图像中添加一个数字水印来证明和宣布图像的所有权。水标记可以是文本,也可以是公开或隐蔽地放在图像中的图像,预计会面临删除的挑战。本文提议结合进化神经网络(CNNs)和波盘变换,以获得嵌入和提取水印标记的水标记网络。网络独立于主机图像分辨率之外,可以接受所有类型的水印,在保持性能时只有11层。用两个术语来衡量业绩;提取的水印与原水标记和水标记图像之间的相似性。