Search Results (151)

In this paper, a security-enhanced three-dimensional (3D) image hiding and encryption method is proposed by combining a layer-based phase-only hologram (POH) under structured light illumination with chaotic encryption and digital image watermarking technology. In the proposed method, the original 3D plaintext image is firstly encoded into a layer-based POH and then further encrypted into an encrypted phase with the help of a chaotic random phase mask (CRPM). Subsequently, the encrypted phase is embedded into a visible ciphertext image by using a digital image watermarking technology based on discrete wavelet transform (DWT) and singular value decomposition (SVD), leading to a 3D image hiding with high security and concealment. The encoding of POH and the utilization of CRPM can substantially enhance the level of security, and the DWT-SVD-based digital image watermarking can effectively hide the information of the 3D plaintext image in a visible ciphertext image, thus improving the imperceptibility of valid information. It is worth noting that the adopted structured light during the POH encoding possesses many optical parameters, which are all served as the supplementary keys, bringing about a great expansion of key space; meanwhile, the sensitivities of the wavelength key and singular matrix keys are also substantially enhanced thanks to the introduction of structured light, contributing to a significant enhancement of security. Numerical simulations are performed to demonstrate the feasibility of the proposed 3D image hiding method, and the simulation results show that the proposed method exhibits high feasibility and apparent security-enhanced effect as well as strong robustness. Full article

A dual watermark and DNA image encryption based on a chaotic map is proposed. Firstly, a new discrete chaotic map is proposed, and the dynamic characteristics are analyzed. Then, the hash value changes initial conditions, and the pseudo-random sequence is generated. The encrypted copyright image is fused with the feature value of the original image and then encrypted again to form zero-watermarking, which is registered with the copyright certification authority. The zero-watermarking is taken as a robust watermark and embedded into the original image based on a chaotic sequence to ensure its invisibility. Finally, a cross-mutation DNA encryption is proposed. The experimental results verify the performance of encryption and dual watermark copyright authentication, and the ability to resist attacks. Full article

Infrared (IR) images record the temperature radiation distribution of the object being captured. The hue and color difference in the image reflect the caloric and temperature difference, respectively. However, due to the thermal diffusion effect, the target information in IR images can be relatively large and the objects’ boundaries are blurred. Therefore, IR images may undergo some image enhancement operations prior to use in relevant application scenarios. Furthermore, Infrared Enhancement (IRE) algorithms have a negative impact on the watermarking information embedded into the IR image in most cases. In this paper, we propose a novel multi-scale robust watermarking model under IRE attack, called IEWNet. This model trains a preprocessing module for extracting image features based on the conventional Undecimated Dual Tree Complex Wavelet Transform (UDTCWT). Furthermore, we consider developing a noise layer with a focus on four deep learning and eight classical attacks, and all of these attacks are based on IRE algorithms. Moreover, we add a noise layer or an enhancement module between the encoder and decoder according to the application scenarios. The results of the imperceptibility experiments on six public datasets prove that the Peak Signal to Noise Ratio (PSNR) is usually higher than 40 dB. The robustness of the algorithms is also better than the existing state-of-the-art image watermarking algorithms used in the performance evaluation comparison. Full article

In the field of 3D technology, depth-image-based rendering (DIBR) has been widely adopted due to its inherent advantages including low data volume and strong compatibility. However, during network transmission of DIBR 3D images, both center and virtual views are susceptible to unauthorized copying and distribution. To protect the copyright of these images, this paper proposes a channel interaction mamba-guided generative adversarial network (CIMGAN) for DIBR 3D image watermarking. To capture cross-modal feature dependencies, a channel interaction mamba (CIM) is designed. This module enables lightweight cross-modal channel interaction through a channel exchange mechanism and leverages mamba for global modeling of RGB and depth information. In addition, a feature fusion module (FFM) is devised to extract complementary information from cross-modal features and eliminate redundant information, ultimately generating high-quality 3D image features. These features are used to generate an attention map, enhancing watermark invisibility and identifying robust embedding regions. Compared to the current state-of-the-art (SOTA) 3D image watermarking methods, the proposed watermark model shows superior performance in terms of robustness and invisibility while maintaining computational efficiency. Full article

This study improves existing protection strategies for image processing models by embedding invisible watermarks into model outputs to verify the sources of images. Most current methods rely on CNN-based architectures, which are limited by their local perception capabilities and struggle to effectively capture global information. To address this, we introduce the Swin-UNet, originally designed for medical image segmentation tasks, into the watermark embedding process. The Swin Transformer’s ability to capture global information enhances the visual quality of the embedded image compared to CNN-based approaches. To defend against surrogate attacks, data augmentation techniques are incorporated into the training process, enhancing the watermark extractor’s robustness specifically against surrogate attacks. Experimental results show that the proposed watermarking approach reduces the impact of watermark embedding on visual quality. On a deraining task with color images, the average PSNR reaches 45.85 dB, while on a denoising task with grayscale images, the average PSNR reaches 56.60 dB. Additionally, watermarks extracted from surrogate attacks closely match those from the original framework, with an accuracy of 99% to 100%. These results confirm the Swin Transformer’s effectiveness in preserving visual quality. Full article

Ensuring the security of remote sensing images is essential to prevent unauthorized access, tampering, and misuse. Deep learning-based digital watermarking offers a promising solution by embedding imperceptible information to protect data integrity. This paper proposes SIR-DCGAN, an attention-guided robust watermarking method for remote sensing image protection. It incorporates an IR-FFM feature fusion module to enhance feature reuse across different layers and an SE-AM attention mechanism to emphasize critical watermark features. Additionally, a noise simulation sub-network is introduced to improve resistance against common and combined attacks. The proposed method achieves high imperceptibility and robustness while maintaining low computational cost. Extensive experiments on both remote sensing and natural image datasets validate its effectiveness, with performance consistently surpassing existing approaches. These results demonstrate the practicality and reliability of SIR-DCGAN for secure image distribution and copyright protection. Full article

This paper presents a novel forensic watermarking method for digital evidence distribution in non-cloud environments. The approach addresses the critical need for the secure sharing of Joint Photographic Experts Group (JPEG) images in forensic investigations. The method utilises an adaptive Discrete Cosine Transform–Discrete Wavelet Transform (DCT-DWT) domain technique to embed a 64-bit watermark in both stand-alone JPEGs and those within forensic disk images. This occurs without alterations to disk structure or complications to the chain of custody. The system implements uniform secure randomisation and recipient-specific watermarks to balance security with forensic workflow efficiency. This work presents the first implementation of forensic watermarking at the disk image level that preserves structural integrity and enables precise leak source attribution. It addresses a critical gap in secure evidence distribution methodologies. The evaluation occurred on extensive datasets: 1124 JPEGs in a forensic disk image, 10,000 each of BOSSBase 256 × 256 and 512 × 512 greyscale images, and 10,000 COCO2017 coloured images. The results demonstrate high imperceptibility with average Peak Signal-to-Noise Ratio (PSNR) values ranging from 46.13 dB to 49.37 dB across datasets. The method exhibits robust performance against geometric attacks with perfect watermark recovery (Bit Error Rate (BER) = 0) for rotations up to 90° and scaling factors between 0.6 and 1.5. The approach maintains compatibility with forensic tools like Forensic Toolkit FTK and Autopsy. It performs effectively under attacks including JPEG compression (QF ≥ 60), filtering, and noise addition. The technique achieves high feature match ratios between 0.684 and 0.690 for a threshold of 0.70, with efficient processing times (embedding: 0.0347 s to 0.1187 s; extraction: 0.0077 s to 0.0366 s). This watermarking technique improves forensic investigation processes, particularly those that involve sensitive JPEG files. It supports leak source attribution, preserves evidence integrity, and provides traceability throughout forensic procedures. Full article

With the exponential growth in transactions and exchanges carried out via the Internet, the risks of the falsification and distortion of information are multiplying, encouraged by widespread access to the virtual world. In this context, digital image watermarking has emerged as an essential solution for protecting digital content by enhancing its durability and resistance to manipulation. However, no current digital watermarking technology offers complete protection against all forms of attack, with each method often limited to specific applications. This field has recently benefited from the integration of deep learning techniques, which have brought significant advances in information security. This article explores the implementation of digital watermarking in embedded systems, addressing the challenges posed by resource constraints such as memory, computing power, and energy consumption. We propose optimization techniques, including frequency domain methods and the use of lightweight deep learning models, to enhance the robustness and resilience of embedded systems. The experimental results validate the effectiveness of these approaches for enhanced image protection, opening new prospects for the development of information security technologies adapted to embedded environments. Full article

The rapid development of diffusion models in image generation and processing has led to significant security concerns. Diffusion models are capable of producing highly realistic images that are indistinguishable from real ones. Although deploying a watermarking system can be a countermeasure to verify the ownership or the origin of images, the regeneration attacks arising from diffusion models can easily remove the embedded watermark from the images, without compromising their perceptual quality. Previous watermarking methods that hide watermark information in the carrier image are vulnerable to these newly emergent attacks. To address these challenges, we propose a robust and traceable watermark framework based on the latent diffusion model, where the spread-spectrum watermark is coupled with the diffusion noise to ensure its security and imperceptibility. Since the diffusion model is trained to reduce information entropy from disordered data to restore its true distribution, the transparency of the hidden watermark is guaranteed. Benefiting from the spread spectrum strategy, the decoder structure is no longer needed for watermark extraction, greatly alleviating the training overhead. Additionally, the robustness and transparency are easily controlled by a strength factor, whose operating range is studied in this work. Experimental results demonstrate that our method performs not only against common attacks, but also against regeneration attacks and semantic-based image editing. Full article

Due to their considerable costs, deep neural networks (DNNs) are valuable assets that need to be protected in terms of intellectual property (IP). From this statement, DNN watermarking gains significant interest since it allows DNN owners to prove their ownership. Various methods that embed ownership information in the model behavior have been proposed. They need to fill several requirements, among them the security, which represents an attacker’s difficulty in breaking the watermarking scheme. There is also the robustness requirement, which quantifies the resistance against watermark removal techniques. The problem is that the proposed methods generally fail to meet these necessary standards. This paper presents RoSe-Mix, a robust and secure deep neural network watermarking technique designed for black-box settings. It addresses limitations in existing DNN watermarking approaches by integrating key features from two established methods: RoSe, which uses cryptographic hashing to ensure security, and Mixer, which employs image Mixup to enhance robustness. Experimental results demonstrate that RoSe-Mix achieves security across various architectures and datasets with a robustness to removal attacks exceeding 99%. Full article

Interior design, which integrates art and science, is vulnerable to infringements such as copying and tampering. The unique and often intricate nature of these designs makes them vulnerable to unauthorized replication and misuse, posing significant challenges for designers seeking to protect their intellectual property. To solve the above problems, we propose a deep learning-based zero-watermark copyright protection method. The method aims to embed undetectable and unique copyright information through image fusion technology without destroying the interior design image. Specifically, the method fuses the interior design and a watermark image through deep learning to generate a highly robust zero-watermark image. This study also proposes a zero-watermark verification network with U-Net to verify the validity of the watermark and extract the copyright information efficiently. This network can accurately restore watermark information from protected interior design images, thus effectively proving the copyright ownership of the work and the copyright ownership of the interior design. According to verification on an experimental dataset, the zero-watermark copyright protection method proposed in this study is robust against various image-oriented attacks. It avoids the problem of image quality loss that traditional watermarking techniques may cause. Therefore, this method can provide a strong means of copyright protection in the field of interior design. Full article

In this digital era, social images are the most vital information carrier on multimedia social platforms. More and more users are interested in sharing social images with mobile terminals on multimedia social platforms. Social image sharing also faces potential risks from malicious use, such as illegal sharing, piracy, and misappropriation. This paper mainly concentrates on secure social image sharing. To address how to share social images in a safe way, a social image security scheme is proposed. The technology addresses the social image security problem and the active tracing problem. First, discrete wavelet transform (DWT) is performed directly from the JPEG image. Then, the high-bit planes of the LL, LH, and HL are permuted with cellular automation (CA), bit-XOR, and singular value decomposition (SVD) computing, and their low-bit planes are chosen to embed a watermark. In the end, the encrypted and watermarked image is again permuted with cellular automation in the discrete cosine transform (DCT) domain. Experimental results and security analysis show that the social image security method not only has good performance in robustness, security, and time complexity but can also actively trace the illegal distribution of social images. The proposed social image security method can provide double-level security for multimedia social platforms. Full article

The rapid advancement of AI-generated content (AIGC) has significantly improved the realism and accessibility of synthetic images. While large image generation models offer immense potential in creative industries, they also introduce serious challenges, including copyright infringement, content authentication, and the traceability of generated images. Digital watermarking has emerged as a promising approach to address these concerns by embedding imperceptible yet detectable signatures into generated images. This survey provides a comprehensive review of three core areas: (1) the evolution of image generation technologies, highlighting key milestones such as the transition from GANs to diffusion models; (2) traditional and state-of-the-art digital image watermarking algorithms, encompassing spatial domain, transform domain, and deep learning-based approaches; (3) watermarking methods specific to AIGC, including ownership authentication of AI model and diffusion model, and watermarking of AI-generated images. Additionally, we examine common performance evaluation metrics used in this field, such as watermark capacity, watermark detection accuracy, fidelity, and robustness. Finally, we discuss the unresolved issues and propose several potential directions for future research. We look forward to this paper offering valuable reference for academics in the field of AIGC watermarking and related fields. Full article

This study presents a ring template watermarking method utilizing semantic segmentation and elliptical fitting to address the inadequate resilience of digital watermarking techniques for remote sensing images against geometric attacks and affine transformations. The approach employs a convolutional neural network to determine the coverage position of the annular template watermark automatically. Subsequently, it applies the least squares approach to align with the relevant elliptic curve of the annular watermark, facilitating the restoration of the watermark template post-deformation due to an attack. Ultimately, it acquires the watermark information by analyzing the binarized image according to the coordinates. The experimental results indicate that, despite various geometric and affine modification attacks, the NC mean value of watermark extraction exceeds 0.83, and the PSNR value surpasses 35, thereby ensuring substantial invisibility and enhanced robustness. In addition, the methods presented in this paper provide useful references for imaging data in other fields. Full article

The integration of Internet of Medical Things (IoMT) technology has revolutionized healthcare, allowing rapid access to medical images and enhancing remote diagnostics in telemedicine. However, this advancement raises serious cybersecurity concerns, particularly regarding unauthorized access and data integrity. This paper presents a novel, user-friendly, visible watermarking scheme for medical images—Visual and User-Friendly Medical Image Watermarking Scheme (VUF-MIWS)—designed to secure medical image ownership while maintaining usability for diagnostic purposes. VUF-MIWS employs a unique combination of inpainting and data hiding techniques to embed hospital logos as visible watermarks, which can be removed seamlessly once image authenticity is verified, restoring the image to its original state. Experimental results demonstrate the scheme’s robust performance, with the watermarking process preserving critical diagnostic information with high fidelity. The method achieved Peak Signal-to-Noise Ratios (PSNR) above 70 dB and Structural Similarity Index Measures (SSIM) of 0.99 for inpainted images, indicating minimal loss of image quality. Additionally, VUF-MIWS effectively restored the ROI region of medical images post-watermark removal, as verified through test cases with restored watermarked regions matching the original images. These findings affirm VUF-MIWS’s suitability for secure telemedicine applications. Full article