Digital image watermarking has justified its suitability for copyright protection and copy control of digital images. In the past years, various watermarking schemes were proposed to enhance the fidelity and the robustness of watermarked images against different types of attacks such as additive noise, filtering, and geometric attacks. It is highly important to guarantee a sufficient level of robustness of watermarked images against such type of attacks. Recently, Deep learning and neural networks achieved noticeable development and improvement, especially in image processing, segmentation, and classification. Therefore, in this paper, we studied the effect of a Fully Convolutional Neural Network (FCNN), as a denoising attack, on watermarked images. This deep architecture improves the training process and denoising performance, through which the encoder–decoder remove the noise while preserving the detailed structure of the image. FCNNDA outperforms the other types of attacks because it destroys the watermarks while preserving a good quality of the attacked images. Spread Transform Dither Modulation (STDM) and Spread Spectrum (SS) are used as watermarking schemes to embed the watermarks in the images using several scenarios. This evaluation shows that such type of denoising attack preserves the image quality while breaking the robustness of all evaluated watermarked schemes. It could also be considered a deleterious attack.

数字图像水印已证明其适用于数字图像的版权保护和复制控制。在过去的几年中，提出了各种水印方案以增强水印图像针对不同类型的攻击（例如加性噪声，过滤和几何攻击）的保真度和鲁棒性。确保带水印的图像具有足够的鲁棒性以抵抗此类攻击非常重要。最近，深度学习和神经网络取得了显着的发展和进步，尤其是在图像处理，分割和分类方面。因此，在本文中，我们研究了全卷积神经网络（FCNN）对水印图像的去噪攻击。这种深层的架构改善了训练过程并降低了降噪效果，通过这种方式，编码器-解码器可以消除噪声，同时保留图像的详细结构。FCNNDA胜过其他类型的攻击，因为它可以破坏水印，同时又能保持高质量的被攻击图像。扩展变换抖动调制（STDM）和扩展频谱（SS）被用作水印方案，以在多种情况下将水印嵌入图像中。该评估表明，这种类型的降噪攻击在保留所有评估水印方案的鲁棒性的同时，可以保留图像质量。它也可以被视为有害攻击。扩展变换抖动调制（STDM）和扩展频谱（SS）被用作水印方案，以在多种情况下将水印嵌入图像中。该评估表明，这种类型的降噪攻击在保留所有评估水印方案的鲁棒性的同时，可以保留图像质量。它也可以被视为有害攻击。扩展变换抖动调制（STDM）和扩展频谱（SS）被用作水印方案，以在多种情况下将水印嵌入图像中。该评估表明，这种类型的降噪攻击在保留所有评估水印方案的鲁棒性的同时，可以保留图像质量。它也可以被视为有害攻击。