Survey of IPX (IP eXchange) as an Emerging International Interconnection between Telecommunication Networks

The heterogeneity of terminal devices poses a huge collaboration challenge for designers and engineers who routinely use these devices in performing professional activities. This study explored how heterogeneous terminal devices such as desktop computers, laptop computers, smartphones and tablet PCs could be used together to facilitate collaborative product visualisation in engineering design. The focus was specifically on addressing the challenge of adapting visual product information content in context. The techniques currently used in content adaptation were reviewed and analysed and some of the main challenges faced in adapting product visual information content in context identified. A scheme for content adaptation in context was subsequently created, with a view to enable ubiquitous remote product data visualisation across heterogeneous terminal devices and to support collaboration in engineering design processes. An application example is presented to illustrate the applicability of the proposed sc...

Visual content adaptation in context to facilitate collaboration in engineering design

As digital images become increasingly sophisticated, they raise significant security concerns, including the copyright violation, data leakage and identity theft. Deep learning-based data hiding techniques conceals mark within media carriers, enabling both error-free mark extraction and lossless carrier restoration. However, the challenge of enhancing watermark robustness data while ensuring imperceptibility, security, embedding capacity, and model security becomes increasingly pronounced in deep learning environment. In this paper, we present GANMarked, a robust watermarking method embedding a secure mark into the media carriers, based on a generative adversarial network (GAN). First, we utilize an improved autoencoder-based network for secure generation of encoded mark by encoding two individual watermarks into one. Second, the encoded mark imperceptibly embedding into the media carriers using GAN network. Third, the extraction network considers only the marked media as input and robustly recovers the hidden mark at the receiver side. In addition to media security, we fine-tuned the deep watermarking network using secret trigger key to verify the ownership of suspicious models if any piracy or infringements occur. Lastly, decoder network reconstructs the encoded media into the individual one. Our method has been empirically validated across multiple standard datasets, consistently maintaining high imperceptibility, robustness and security, even with variations in hybrid noise during mark extraction. Further, the results demonstrate that the proposed method significantly outperforms other existing methods in terms of imperceptibility and robustness while ensuring reversibility and security. 

GANMarked: Using Secure GAN for Information Hiding in Digital Images

Split ways: Using GAN watermarking for digital image protection with privacy-preserving split model training

The ongoing issue of unauthorized image distribution poses a serious modern challenge. This research introduces an innovative dual image watermarking technique that determines a potential embedding area in a small 4×4 DCT block and employs a hybrid approach of NSST-LWT-DCT-SVD to ensure efficient copyright protection. The watermark images are integrated using the red and green channels. These channels are then transformed using NSST-LWT to extract the low-frequency sub-band. Following this, the low-frequency section is divided into 4×4 blocks. The Human Visual System (HVS) is then used to select the appropriate blocks, which are transformed using DCT. Certain coefficients from the middle frequency of the DCT are chosen for decomposition with SVD. The singular values are then employed for embedding the watermark bits. Experimental results indicate that the proposed technique is a viable option for image watermarking, achieving average PSNR and SSIM values of up to 45.9 dB and 0.9962, respectively, while closely approaching a value of 1 for NC performance. 

Secure and Robust Watermarking Using NSST-LWT-DCT-SVD for Copyright Protection

Robust zero-watermarking algorithm for diffusion-weighted images based on multiscale feature fusion

Deep watermarking has been successfully applied to real-value domain (e.g., CT, MRI, DWI) medical images for copyright protection; however, manifold-value domain images, such as diffusion tensor imaging (DTI), have received less academic attention. The non-Euclidean nature of DTI images hinders deep models from generating plausible DTI images because it is difficult to guarantee that each generated DTI image voxel lies on a 3 × 3 symmetric positive definite manifold. In this paper, we propose a robust watermarking scheme based on voxel space transformation for DTI images. First, a voxel space transformation is proposed, which effectively solves the problem of fiber orientation estimation in Riemann networks for DTI images and obtains the eigenvalues and eigenvectors of each voxel using singular value decomposition (SVD) to transform the DTI voxels from the 3 × 3 symmetric positive definite manifold space to Euclidean space. Second, SVD is combined with a deep neural network to extract the high-level features of each voxel eigenvalue in the DTI images and embedding watermarking messages, avoiding the false positive errors problem in traditional watermarking algorithms based on an SVD transform. Finally, using a deep neural network combining multiscale dilated convolution, dense residual connection and channel attention, the algorithm demonstrates high robustness against various intentional or unintentional attacks on DTI images while ensuring the good visual quality and diffusion characteristics of the DTI images embedded with watermarking messages. Experiments show that the watermarking message extraction accuracy reaches 97.8% for Crop (p = 0.7) attacks and 95.4% for Gaussian Blur (k = 7) attacks. 

VSTNet: Robust watermarking scheme based on voxel space transformation for diffusion tensor imaging images

An efficient transcoding algorithm between AMR-NB and G.729ab

For copyright protection of diffusion-weighted imaging (DWI) images, traditional robust watermarking techniques result in irreversible distortions, while reversible watermarking methods exhibit poor robustness. We propose a two-stage lossless watermarking algorithm based on a Transformer network to solve this problem. The first stage of the algorithm is to train the robust watermarking network, embed the watermark into the cover image in the wavelet domain, and design the frequency information enhancement module to improve the reconstruction quality. In the second stage, based on the pre-trained robust watermarking network, the difference image between the watermarked image and the cover image is reversibly embedded into the watermarked image as the compensation information to losslessly recover the cover image. The difference image is compressed using DCT and Huffman coding to reduce the compensation information. Finally, the watermark extraction network is trained on the second embedding result to avoid weakening the robustness of the first stage caused by the reversible embedding. The experimental results demonstrate that the PSNR of the watermarked image reaches 60.18 dB. Under various types of image attacks, the watermark extraction BER is below 0.003, indicating excellent robustness. The cover image can be recovered losslessly under no attack.

Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain

International Journal of Pattern Recognition and Artificial IntelligenceAccepted Papers No AccessTSAD: Two-Stage Separable Adversarial Distortion-Based Robust Watermarking Framework for Diffusion Tensor ImagingLong Zheng, Zhi Li, Zhangyu Liu, Dandan Li, Li Zhang, Hong Yue, Fei Cheng, Qin Mao, Xuekai Wei, and Mingliang ZhouLong Zheng, Zhi Li Search for more papers by this author , Zhangyu Liu Search for more papers by this author , Dandan Li Search for more papers by this author , Li Zhang Search for more papers by this author , Hong Yue Search for more papers by this author , Fei Cheng Search for more papers by this author , Qin Mao Search for more papers by this author , Xuekai Wei Search for more papers by this author , and Mingliang Zhouhttps://orcid.org/0000-0002-1874-3641 Search for more papers by this author https://doi.org/10.1142/S0218001424540119Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 19 February 2024 Accepted 12 March 2024 PDF download 

TSAD: Two-Stage Separable Adversarial Distortion-Based Robust Watermarking Framework for Diffusion Tensor Imaging

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