Robust Zero Watermarking Algorithm for Medical Images Based on Improved NasNet-Mobile and DCT

In order to safeguard image copyrights, zero-watermarking technology extracts robust features and generates watermarks without altering the original image. Traditional zero-watermarking methods rely on handcrafted feature descriptors to enhance their performance. With the advancement of deep learning, this paper introduces “ZWNet”, an end-to-end zero-watermarking scheme that obviates the necessity for specialized knowledge in image features and is exclusively composed of artificial neural networks. The architecture of ZWNet synergistically incorporates ConvNeXt and LK-PAN to augment the extraction of local features while accounting for the global context. A key aspect of ZWNet is its watermark block, as the network head part, which fulfills functions such as feature optimization, identifier output, encryption, and copyright fusion. The training strategy addresses the challenge of simultaneously enhancing robustness and discriminability by producing the same identifier for attacked images and distinct identifiers for different images. Experimental validation of ZWNet’s performance has been conducted, demonstrating its robustness with the normalized coefficient of the zero-watermark consistently exceeding 0.97 against rotation, noise, crop, and blur attacks. Regarding discriminability, the Hamming distance of the generated watermarks exceeds 88 for images with the same copyright but different content. Furthermore, the efficiency of watermark generation is affirmed, with an average processing time of 96 ms. These experimental results substantiate the superiority of the proposed scheme over existing zero-watermarking methods.

Section: Zero-watermarkingmentioning

confidence: 99%

ZWNet: A Deep-Learning-Powered Zero-Watermarking Scheme with High Robustness and Discriminability for Images

Li,

Sun,

Wang

et al. 2024

“…A distinct concept is zero watermarking, where no watermark is directly embedded [26,27]. Instead, a relationship is established and stored between the original content and the watermark (a master share).…”

Section: Typical Metrics and Factorsmentioning

confidence: 99%

A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking

Zhong,

Das,

Alrasheedi

et al. 2023

This paper presents a comprehensive survey of deep learning-based image watermarking; this technique entails the invisible embedding and extraction of watermarks within a cover image, aiming for a seamless combination of robustness and adaptability. We navigate the complex landscape of this interdisciplinary domain, linking historical foundations, current innovations, and prospective developments. Unlike existing literature, our study concentrates exclusively on image watermarking with deep learning, delivering an in-depth, yet brief analysis enriched by three fundamental contributions. First, we introduce a refined categorization, segmenting the field into embedder–extractor, deep networks for feature transformation, and hybrid methods. This taxonomy, inspired by the varied roles of deep learning across studies, is designed to infuse clarity, offering readers technical insights and directional guidance. Second, our exploration dives into representative methodologies, encapsulating the diverse research directions and inherent challenges within each category to provide a consolidated perspective. Lastly, we venture beyond established boundaries, outlining emerging frontiers and providing detailed insights into prospective research avenues.

“…This ensures the accurate expression of the original visual effect of the image. This technology is widely applied in sensitive digital image domains where information tampering is a concern, such as military radar images, patient pathology images [3], and artistic heritage images.…”

Section: Introductionmentioning

confidence: 99%

A Zero-Watermarking Algorithm Based on Scale-Invariant Feature Reconstruction Transform

Li,

Wang

2024

In order to effectively protect and verify the copyright information of multimedia digital works, this paper proposes a zero-watermarking algorithm based on carrier image feature point descriptors. The constructed feature matrix of this algorithm consists of two parts: the feature descriptor vector calculated from scale-invariant feature reconstruction transform (SIFRT) and the multi-radius local binary pattern (MrLBP) descriptor vector. The algorithm performs a standardization, feature decomposition, and redundancy reduction on the traditional keypoint descriptor matrix, combines it with the texture feature matrix, and achieves the dimensional matching of copyright information. The advantage of this algorithm lies in its non-modification of the original data. Compared to computing global features, the local features computed from a subset of key points reduce the amount of attack interference introduced during copyright verification, thereby reducing the number of erroneous pixel values that are introduced. The algorithm introduces a timestamp mechanism when uploading the generated zero-watermarking image to a third-party copyright center, preventing subsequent tampering. Experimental data analysis demonstrates that the algorithm exhibits good discriminability, security, and robustness.