Estimation of the Embedding capacity is an important problem specifically in reversible multi-pass watermarking and is required for analysis before any image can be watermarked. In this paper, we propose an efficient method for estimating the embedding capacity of a given cover image under multi-pass embedding, without actually embedding the watermark. We demonstrate this for a class of reversible watermarking schemes which operate on a disjoint group of pixels, specifically for pixel pairs. The proposed algorithm iteratively updates the co-occurrence matrix at every stage, to estimate the multi-pass embedding capacity, and is much more efficient vis-a-vis actual watermarking. We also suggest an extremely efficient, pre-computable tree based implementation which is conceptually similar to the co-occurrence based method, but provides the estimates in a single iteration, requiring a complexity akin to that of single pass capacity estimation. We also provide bounds on the embedding capacity. We finally show how our method can be easily used on a number of watermarking algorithms and specifically evaluate the performance of our algorithms on the benchmark watermarking schemes of Tian [11] and Coltuc [6].
Teleradiology enables medical images to be transferred over the computer networks for many purposes including clinical interpretation, diagnosis, archive, etc. In telemedicine, medical images can be manipulated while transferring. In addition, medical information security requirements are specified by the legislative rules, and concerned entities must adhere to them. In this research, we propose a new scheme based on 2-dimensional Discrete Wavelet Transform (2D DWT) to improve the robustness and authentication of medical images. In addition, the current research improves security and capacity …
In this paper, we carry out a performance analysis from a probabilistic perspective to introduce the EDHVW methods' expected performances and limitations. Then, we propose a new general error diffusion based halftone visual watermarking (EDHVW) method, Content aware Double-sided Embedding Error Diffusion (CaDEED), via considering the expected watermark decoding performance with specific content of the cover images and watermark, different noise tolerance abilities of various cover image content and the different importance levels of every pixel (when being perceived) in the secret pattern (wat…
The security of patient information is important during the transfer of medical data. A hybrid spatial domain watermarking algorithm that includes encryption, integrity protection and steganography is proposed to strengthen the information originality based on the authentication. The proposed algorithm checks whether the information of patients has been deliberately changed or not. The created code is distributed at every pixel of the medical image and not only in the regions of noninterest pixels, while the image details are still preserved. To enhance the security of the watermarking code, "…
Integrated healthcare systems require the transmission of medical images between medical centers. The presence of watermarks in such images has become important for patient privacy protection. However, some important issues should be considered while watermarking an image. Among these issues, the watermark should be robust against attacks and does not affect the quality of the image. In this paper, a watermarking approach employing a robust dynamic secret code is proposed. This approach is to process every pixel of the digital image and not only the pixels of the regions of non-interest at the…
Watermarking is an operation of embedding an information into an image in a way that allows to identify ownership of the image despite applying some distortions on it. In this paper, we presented a novel end-to-end solution for embedding and recovering the watermark in the digital image using convolutional neural networks. The method is based on spreading the message over the spatial domain of the image, hence reducing the "local bits per pixel" capacity. To obtain the model we used adversarial training and applied noiser layers between the encoder and the decoder. Moreover, we broadened the s…
Due to the recent demand of 3-D models in several applications like medical imaging, video games, among others, the necessity of implementing 3-D mesh watermarking schemes aiming to protect copyright has increased considerably. The majority of robust 3-D watermarking techniques have essentially focused on the robustness against attacks while the imperceptibility of these techniques is still a real issue. In this context, a blind robust 3-D mesh watermarking method based on mesh saliency and Quantization Index Modulation (QIM) for Copyright protection is proposed. The watermark is embedded by q…
Watermarking has become a plausible candidate for ownership verification and intellectual property protection of deep neural networks. Regarding image classification neural networks, current watermarking schemes uniformly resort to backdoor triggers. However, injecting a backdoor into a neural network requires knowledge of the training dataset, which is usually unavailable in the real-world commercialization. Meanwhile, established watermarking schemes oversight the potential damage of exposed evidence during ownership verification and the watermarking algorithms themselves. Those concerns dec…
Robust Reversible Watermarking in Encrypted Images Based on Dual-MSBs Spiral Embedding
School of Communication & Electronic Engineering, East China Normal University（华东师范大学通信与电子工程学院）；Information Technology Center, Shanghai International Studies University（上海外国语大学信息技术中心）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；School of Computer Science, Fudan University（复旦大学计算机科学学院）
CoreMark: Toward Robust and Universal Text Watermarking Technique
School of Aeronautics and Astronautics, Zhejiang University（浙江大学航空宇航学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络与信息安全学院）
Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking
Department of AI, DGIST, Daegu, Republic of Korea（韩国大邱科学技术院人工智能系）；Department of EECS, DGIST, Daegu, Republic of Korea（韩国大邱科学技术院电子工程与计算机科学系）；ICT convergence, University of Ulsan, Ulsan, Republic of Korea（韩国釜山大学信息通信融合学院）
We propose adversarial embedding, a new steganography and watermarking technique that embeds secret information within images. The key idea of our method is to use deep neural networks for image classification and adversarial attacks to embed secret information within images. Thus, we use the attacks to embed an encoding of the message within images and the related deep neural network outputs to extract it. The key properties of adversarial attacks (invisible perturbations, nontransferability, resilience to tampering) offer guarantees regarding the confidentiality and the integrity of the hidd…
PURA: Provably Unbiased and Robust Multi-Bit Watermarking for AI-Generated Text Attribution
Hefei University of Technology（合肥工业大学）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology of China（中国科学技术大学）；Bilkent University（比尔肯特大学）
This paper presents a reversible data hiding in encrypted image (RDHEI), which divides image into non-overlapping blocks. In each block, central pixel of the block is considered as leader pixel and others as follower ones. The prediction errors between the intensity of follower pixels and leader ones are calculated and analyzed to determine a feature for block embedding capacity. This feature indicates the amount of data that can be embedded in a block. Using this pre-process for whole blocks, we vacate rooms before the encryption of the original image to achieve high embedding capacity. Also,…
Revisiting Black-Box Model Ownership Verification through Information Theory
Anhui University of Technology（安徽工业大学）；University of Cambridge（剑桥大学）；Microsoft（微软）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Tylogi AI Lab（Tylogi AI实验室）；The Hong Kong Polytechnic University（香港理工大学）；Southeast University（东南大学）
Reversible data hiding in encrypted image (RDHEI) becomes a hot topic, and a lot of algorithms have been proposed to optimize this technology. However, these algorithms cannot achieve strong embedding capacity. Thus, in this paper, we propose an advanced RDHEI scheme based on lossless pixel conversion (LPC). Different from the previous RDHEI algorithms, LPC is inspired by the planar map coloring question, and it performs a dynamic image division process to divide the original image into irregular regions instead of regular blocks as in the previous RDHEI algorithms. In the process of LPC, pixe…
Machine learning involves expensive data collection and training procedures. Model owners may be concerned that valuable intellectual property can be leaked if adversaries mount model extraction attacks. As it is difficult to defend against model extraction without sacrificing significant prediction accuracy, watermarking instead leverages unused model capacity to have the model overfit to outlier input-output pairs. Such pairs are watermarks, which are not sampled from the task distribution and are only known to the defender. The defender then demonstrates knowledge of the input-output pairs …
