Using Deep learning for image watermarking attack

Abstract

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.

Enhanced Invisibility and Robustness of Digital Image Watermarking Based on DWT-SVD

“…One of biggest challenges to analyzing watermarked images is invisibility and robustness. A number of studies have sought to find a tradeoff or balance between invisibility and robustness [58], including proposing an artificial bee colony (ABC) [59][60][61], employing a firefly 3 Applied Bionics and Biomechanics algorithm [62], and using particle swarm optimization (PSO) [63,64]. The main contributions of the present study are as follows:…”

Comprehensive review of watermarking techniques in deep-learning environments

“…To be specific, a deep network takes the content to be processed in a raw format (an image or an audio signal) as input and maps it. Recently, they have been widely used in data hiding and image processing because of their remarkable potential to mimic human brain learning capacities and interact more naturally 17 . Based on the survey (Fig.…”

Survey on deep learning applications in digital image security

“…However, this method did not perform well at generating the watermark in the frequency domain. Hatoum et al 175 employed a fully CNN trained on th BOSSbase dataset to denoise watermarked images and destroy the watermarks while preserving a satisfied quality of the denoised images. Sharma and Chandrasekaran 176 implemented an enhanced hybrid watermarking scheme using DWT and singular value decomposition methods and proposed an adversarial attack based on a CNN-based autoencoder scheme trained on the CIFAR-10 database that could produce a perceptually close image.…”