Semantic communication (SC) enables efficient information exchange by transmitting compact semantic representations rather than raw data, benefiting applications like autonomous driving and medical diagnosis. However, existing copyright protection methods face two key limitations: traditional transform-domain watermarking fails during semantic extraction, while deep learning-based methods lose robustness when integrated with SC. Most critically, existing solutions cannot protect semantic information itself, the core intellectual property in SC. To address these issues, we propose “Dual-stage Robust Semantic Watermarking” (DRSW), a framework that simultaneously protects the copyright for both semantics and reconstructed images. By embedding a watermark into the frequency domain of semantics, DRSW exhibits high robustness against possible channel noises while preserving semantic consistency and maintaining the reconstruction quality of images. Our work provides a new watermarking paradigm for future copyright protection in SC scenarios.

Existing reversible data hiding methods in encrypted images (RDH-EI) are primarily designed for point-to-point scenarios and not suitable for multiparty communication. To address this issue, a novel RDH-EI scheme based on multi-key (k,n)-threshold decryption (RDH-EITD) is proposed, in which a dual-phase embedding method is designed to support authentication for both the content owner and central server. In RDH-EITD, the private key of Paillier is split intonshares, which are then allocated tondistributed receivers. The image is first encrypted and then embedded with additional data to generate the marked ciphertext, which is uploaded to data server. The server can perform the 2nd-phase embedding. The dual-marked ciphertext is then distributed tonreceivers. Each receiver can extract the 2nd-phase embedded data but cannot decrypt the image individually. Only whenkout ofnreceivers submit their partially decrypted results, can the image be decrypted. Then the 1st-phase embedded data can be extracted and the original image can be recovered losslessly. Security reduction is employed to formally prove the semantic security of RDH-EITD. Experimental results demonstrate that RDH-EITD preserves (k,n)-threshold decryption, enabling resistance againstk− 1 collusion attacks and tolerance ofn−kfailures. The dual-phase embedding achieves embedding rates of λ − 9 bpp (bits per pixel) and 14 bpp respectively with security parameter λ. For an image of lengthLin one-to-ncommunication scenarios, the time complexity of RDH-EITD reachesO(L(λ3+k2)), outperforming existing Paillier-based point-to-point solutions.

The reversibility of robust reversible watermarking (RRW) strictly depends on the premise that the watermarked image has not been attacked. However, in practical applications, multimedia content often suffers from various attacks, and existing RRW technologies completely lose their reversible recovery capabilities. Therefore, this paper proposes a self-recovery robust watermarking (SRRW) algorithm, which provides a new ability to recover the watermarked and attacked image more closely to the original image while the watermark maintains strong robustness to those common signal processing operations and geometric attacks. First, the watermark information is inserted into low-order Zernike moments of a cover image by using quantization watermarking technique so that the watermark is resistant to those additive noise- like operations ( like JPEG compression and Gaussian noises) and invariant to geometric transforms ( like rotation and scaling). Then, the scaled difference vectors between the cover vectors and its quantized watermarked vectors are luminously and ingeniously computed as the distortion compensation information added back to the quantized watermarked vectors for restoration of the cover image. Finally, at the receiver side, based on the watermark bits extracted from the watermarked image or its distorted versions, those Zernike moments embedded data can be restored by the known scaling factor of the difference vector. Furthermore, a self-recovery image resembling the original image more closely can be reconstructed. Experimental results show that the proposed SRRW scheme can effectively restore those distorted images due to the 128-bit watermark embedding, JPEG compression with the quality factor 60 or JPEG2000 compression with the compression ratio 9, while providing strong robustness performance to various kinds of attacks. Compared with the attacked watermarked images, the restored images show PSNR improvements of 1.66 dB, 3.53 dB, and 1.72 dB under JPEG compression ($Q = 90$), JPEG2000 compression ($R = 3$), and AWGN ($\sigma = 0.0001$), respectively.

Recently, the general distortion metric (GDM) has been applied to reversible data hiding (RDH). Multi-histogram modification (MHM) represents a more effective RDH framework compared to traditional single-histogram methods. In this paper, we propose an MHM based on GDM for an adaptive RDH method. First, we analyze the average distortion of embedded bits within each sub-histogram. Our research shows that embedding data in smooth areas should be avoided. Subsequently, we develop a capacity-distortion model for multiple two-dimensional prediction-error histograms (2D PEHs) to evaluate candidate mappings. After embedding 10,000 bits using the proposed RDH scheme, the seven images within the USC-SIPI dataset achieves an average PSNR of 59.373 dB and an average SSIM of 0.99913. Experimental results demonstrate that the proposed RDH method enhances the visual quality of the marked images compared to state-of-the-art methods.

In reversible data hiding (RDH) community, researchers often train the CNN-based predictors with the Mean Square Error (MSE) loss function to evaluate the differences between original and predicted images. This will make the prediction network parameters optimized for all pixels without difference. Considering that the prediction errors in smooth areas are prioritized from the prediction error set for reversible data hiding, in this letter we propose to apply a smoothness factor into the MSE loss function. The smoothness factor used to evaluate the pixel smoothness of an image in steganography is adopted as the loss weight in the new loss function, corresponding to large values in the smooth areas and small values in the texture areas. Experimental results have shown that the CNN-based predictors trained with the proposed loss function can predict pixels more accurately in the smooth areas than using the original loss function. As a bonus, better embedding performance can be achieved by comparing with recent typical CNN-based RDH methods.

Semantic communication (SC) is an emerging communication paradigm that transmits only task-related semantic features to receivers, offering advantages in speed. However, existing robust steganography cannot extract message correctly after SC. To address this issues, we propose a novel steganography framework based on Generating Adversarial Networks (GANs) for SC, called “Image Semantic Steganography”. Our framework embeds message into semantic features to guarantee extraction while considering both pixel-level and semantic-level distortions to enhance security. Experimental results show that our framework not only achieves message extraction successfully and behavioral covertness during and after SC, but also does not impact the implementation of SC.

In the reversible data hiding (RDH) community, both prediction and mapping strategies are vital for reducing distortion. With high prediction performance, small prediction errors can be generated to reduce the embedding distortion. Besides, the efficient mapping strategy can improve the practicality. In this paper, we propose a new RDH method for color images by using convolution neural networks (CNNs) for prediction and an efficient 3D mapping strategy for embedding. At first, each color image is elaborately divided into three isolated image sets so that the proposed deep prediction network (DPN) can exploit more neighboring pixels in the current channel and the correlation between three channels. Then, an efficient 3D mapping strategy is luminously designed by using the symmetry of the 3D prediction error histogram (PEH). The symmetry of 3D PEH has been analyzed in statistical and experimental ways. Based on the proposed deep prediction network and efficient 3D mapping strategy (DPEM), we construct an efficient RDH method for color images. The performance of the proposed DPN is evaluated by comparing it with several predictors on different image datasets. The embedding performance has been demonstrated by hiding information in color images, e.g., the average PSNR value of the Kodak dataset is 63.63 dB with an embedding capacity of 50,000 bits. Furthermore, the experimental results on the ImageNet and PASCAL VOC2012 datasets have shown the proposed RDH method is superior to several state-of-the-art RDH methods. With the introduction of deep learning, the development of the RDH method for color images can be promoted.

Recently, matrix embedding (ME), a well-known steganographic technique, has been employed in reversible data hiding (RDH) for the first time, improving the performance of single histogram modification (SHM) methods. In this letter, the ME-based RDH strategy is extended from SHM to the more effective multiple histograms modification (MHM) to further improve the reversible embedding performance. The capacity-distortion model is first established in the novel scenario. Then, some theoretical results for payload partition and expansion-bins-determination are given. Finally, based on the derived theoretical investigations, an efficient RDH method with low computational complexity is proposed. Experimental results show that the proposed method can achieve better visual quality compared to some state-of-the-art methods.

In reversible data hiding (RDH) community, researchers often embed bits by shifting prediction-error histogram based on the mean-square error metric (MSEM). This will cause more pixel distortion in the smooth areas. Considering that the human eye is more sensitive to the distortion in the smooth areas, in this letter, we propose a new histogram shifting strategy for RDH by referring to the general distortion metric (GDM). With the GDM, data can be embedded by first modifying those pixels in the texture areas. In both theoretical analysis and experimental testing, we have shown that the use of the proposed GDM-based histogram shifting strategy for RDH can further improve the visual quality of marked images in higher SSIM values by comparing with typical MSEM-based histogram shifting methods.

Pixel-value-ordering (PVO) is one of the most popular methods in reversible data hiding (RDH). In PVO based methods, pixels are processed in a block-wise way, so that the local similarities of the images are considered but the global statistical characteristics are ignored. To better utilize the correlations of pixels, this paper proposes a global sorting strategy to combine utilizations of local and global characteristics of the images. For each pixel, its prediction value and local complexity are first calculated based on its local characteristics. Then the image pixels are sorted globally according to their prediction values to generate a single-sorted pixel sequence, in which the pixels with the equal prediction values are sorted again by referring to their local complexities. In such a way, the spatial distances of image pixels are broken so that the global statistical characteristics can be well exploited. With the proposed sorting strategy, we can obtain a more regular 2D histogram by segmenting the sorted sequence for the location-based PVO (LPVO) predictor. Owe to the regular 2D histogram, we have designed an efficient 2D mapping to achieve perfect performance for all the tested images. With the proposed RDH scheme, the PSNR of the image Lena is as high as 61.86 dB and the average PSNR of the Kodak dataset reaches 63.55 dB after embedding 10,000 bits. The superiority of the proposed method has been verified by comparing with recent state-of-the-art RDH methods.

In the field of invertible image decolorization, how to reduce artifacts in the smooth grayscale regions and prevent color distortion at the boundaries of the reconstructed color image is a crucial issue. In this paper, we propose an invertible deep learning network with extraction and hiding of color information. Our approach separates the original color image into the luminance and chromaticity planes by using orthogonal transformation, which enhances the independence and completeness of color and luminance information. Then, the color feature extraction module is developed to minimize color information distortion, while the color hiding module is adopted to hide color information invisibly. Compared with existing deep-learning-based methods, the proposed network can preserve more color information while ensuring the quality of grayscale images by processing color and grayscale information separately. Furthermore, we propose a reversible data hiding strategy that enhances the performance of the reconstructed color images. Our method outperforms learned invertible image decolorization methods, as demonstrated through experiments on the VOC2012, Kodak24, and NCD datasets.

Existing reversible audio watermarking (RAW) techniques are often vulnerable to intentional or even unintentional attacks on the cover object. This paper proposes a robust RAW scheme based on lattices, which is referred to as Meet-in-the-Middle Embedding (MME). In MME, the lattice quantization errors are properly scaled and added back to the quantized host signals such that the receiver can estimate the cover. Scaling factor serves as a key factor to the reversibility of MME, whose feasible range is rigorously justified. Both theoretically and experimentally, we demonstrate the superiority of MME to improved quantization index modulation (IQIM) in terms of signal-to-watermark ratio (SWR) and generalized signal-to-noise ratio (GSNR). Moreover, simulations show that MME also outperforms other state-of-the-arts in SWR, objective difference grade (ODG), and bit error rate (BER).

For copyright protection and perfect recovery of the original image in case of no attacks, it is necessary to develop robust reversible watermarking (RRW) methods that counteract both common signal processing (CSP) and geometric deformation (GD) attacks (RRW-CG). However, to the best of our knowledge, none of the existing RRW methods exploit target attacks as prior knowledge to improve their robustness and embedding capacity. To this end, we propose a two-stage RRW-CG scheme with attack-simulation-based adaptive normalization and embedding. Specifically, the polar harmonic transform (PHT) moments are taken as watermark carriers, and their stability with respect to target attacks is evaluated by performing attack simulation tests on large-scale images. This enables the adaptive normalization of PHT moments to improve the watermark robustness. The PHT moments with high stability are then chosen as watermark carriers, and the conventional spread transform dither modulation (STDM) with one quantization level is optimized to form the enhanced version with multiple quantization levels, in which the embedding strength is determined adaptively via attack simulation tests on the candidate watermarked image. This in turn improves the watermark robustness and increases the embedding capacity. After the robust watermark has been embedded, errors caused by robust watermarking are used as the auxiliary information and then inserted into the robustly watermarked image via the recursive code-based reversible watermarking technique, ensuring the reversibility in case of no attacks. Extensive experimental simulation results show that the proposed scheme outperforms the state-of-the-art RRW methods in terms of robustness against CSP such as AWGN, JPEG, JPEG2000, mean filtering, and median filtering as well as GD including rotation and scaling under the same invisibility, reversibility, and embedding capacity. This indicates that, by exploiting target attacks as prior knowledge and designing the attack-simulation-based adaptive normalization and embedding, the proposed novel RRW is feasible and effective.

In the field of reversible data hiding (RDH), how to design an efficient image prediction method is an enduring research topic. In this paper, we propose a new CNN-based predictor consisting of an efficient image division strategy and a well-designed prediction network. The image division strategy optimizes the distribution of pixels belonging to different sets, which increases the amount of available adjacent pixels in the image prediction. In addition, with the utilization of the well-designed compensation module, the prediction network performs better and consumes less memory. The experiment results demonstrate that our approach achieves better prediction performance compared with the existing predictors. Furthermore, we have developed an RDH algorithm by combining the proposed CNN-based predictor with the location-based pixel value ordering (LPVO) embedding strategy. This RDH algorithm outperforms the state-of-the-art predictor-based RDH algorithm in embedding performance.

After the prediction, how to modify the 2D prediction error histogram (PEH) for reversible data hiding (RDH) is an important issue. In this letter, we propose an efficient 2D mapping strategy by considering the error pairs on two diagonal lines of the 2D PEH. The proposed 2D mapping can quickly adapt to the frequencies of the error pairs for different mapping area sizes. As a bonus, a satisfactory embedding performance can be achieved since in each group of two symmetrical error pairs the one with larger frequencies has priority to select more mapping ways. Experimental results have shown that it is low time-consuming and achieves better embedding performance in comparison with several classical and advanced 2D mappings.

In reversible data hiding (RDH), the use of general distortion metric for improvement of image visual quality is a new issue. In this letter, a new RDH method of grayscale images using general distortion metric is proposed. The proposed method can adaptively compute different distortions for different pixels by considering the influence of surrounding pixels. Due to the fact that the pixel distortion in smooth areas is more obvious than that in texture areas under the same modification, the modifications on pixels are adaptively adjusted by combining a minimum distortion coding to achieve better visual quality. Experimental results show that the proposed method can reduce modifications on smooth areas and achieve better embedding performance for grayscale images in comparison with several classical and advanced RDH methods

The robust reversible watermarking (RRW) requires high robustness and capacity on the condition of reversibility and imperceptibility, which still remains a big challenge nowadays. In this paper, we propose a two-stage RRW scheme that improves robustness and capacity through embedding optimization and rounded error compensation. The first stage inserts a robust watermark into the selected Pseudo-Zernike moments (PZMs) by using an adaptive normalization method and an optimized embedding strategy. Specifically, the adaptive normalization method achieves both an invariance to pixel amplitude variation and a balance between robustness and imperceptibility, and the optimized embedding strategy reduces embedding distortions remarkably. The watermarked PZMs are inversely transformed to generate the robustly watermarked image, in which rounded errors caused in the inverse transformation is compensated elaborately and thus a larger capacity can be obtained at the same embedding distortion. The second stage embeds a reversible watermark consisting of errors between the robust watermark embedded image and the original one, aiming at achieving the reversibility in case of no attacks. Extensive experimental simulations show that the proposed scheme provides strong robustness against common signal processing, including AWGN, salt-and-pepper noise, JPEG, JPEG2000, median filtering, mean filtering, geometrical transformations involving rotation and scaling, and a compressive sensing attack exemplified by two-dimensional compressive sensing, which outperforms the state-of-the-art schemes. Our code is available athttps://github.com/yichao-tang/PZMs-RRW.

In the field of reversible data hiding (RDH), how to predict an image and embed a message into the image with smaller distortion are two important aspects. In this paper, we propose a novel and efficient RDH method by innovating an intelligent predictor and an adaptive embedding way. In the prediction stage, we first constructed a convolutional neural network (CNN) based predictor by reasonably dividing an image into four parts. In such a way, each part can be predicted by using the other three parts as the context for the improvement of the prediction performance. Compared with existing predictors, the proposed CNN predictor can use more neighboring pixels for the prediction by exploiting its multi-receptive fields and global optimization capacities. In the embedding stage, we also developed a prediction-error-ordering (PEO) based adaptive embedding strategy, which can better adapt image content and thus efficiently reduce the embedding distortion by elaborately and luminously applying background complexity to select and pair those smaller prediction errors for data hiding. With the proposed CNN prediction and embedding ways, the RDH method presented in this paper provides satisfactory results in improving the visual quality of data hidden images, e.g., the average PSNR value for the Kodak benchmark dataset can reach as high as 63.59 dB with an embedding capacity of 10,000 bits. Extensive experimental results have shown that the RDH method proposed in this paper is superior to those existing state-of-the-art works.

Invertible color-to-grayscale conversion is a research issue in grayscale image colorization, which is a complex problem resulting from information loss. This paper presents an innovative invertible color-to-grayscale conversion idea by independently compressing the chromaticity plane and hiding it into the corresponding luminance plane as a watermark. Since the chromaticity and luminance planes are orthogonal in the proposed method, they can be processed efficiently without mutual influence. By using an efficient lossy compression operation, we can save more chromatic information. By using two high-capacity data hiding techniques (reversible watermarking (RW) and least significant bit (LSB) substitution), we can embed the compressed chromatic information into the luminance plane perfectly. For the purpose of integrality authentication, the luminance plane is hashed as part of the embedded information before the embedding. Experimental results have shown that higher quality of reconstructed color images can be achieved by using RW, but the quality of the synthesized grayscale drops sharply. By using LSB substitution, we can obtain high-quality synthesized grayscale and reconstructed color images simultaneously. Furthermore, we have compared the proposed LSB-based scheme with several recently reported state-of-the-art methods to validate the superiority of the proposed approach.

In reversible data hiding (RDH) schemes, how to select those smooth pixels, pixel pairs or pixel blocks in order to improve performance is an important issue. For pixel-value-ordering (PVO) based RDH schemes, two existing techniques appear to be defective since only two reference pixels in a block or the right and bottom neighbors of a block are exploited as the context for block selection, and their performance might be only adequate for those smooth images. For rough images, the embedding performance could be affected. In this paper, an efficient block selection method by computing a block’s smoothness with a full-enclosing context (FEC) way is proposed. Obtained results show that the proposed FEC strategy can better estimate a block’s smoothness for PVO-based schemes. Furthermore, a more scalable pairing way is presented for the recently reported location-based PVO predictor. The proposed PVO scheme can be implemented by dividing the cover image and embedding bits into two different types of blocks, respectively. Experimental results show that the marked images by the proposed two-stage PVO scheme have higher visual quality, e.g., the average PSNR for the Kodak image database is 63.31 dB after embedding 10,000 bits, and the gain is 0.16 dB against the best result in the literature. Compared with some state-of-the-art RDH works, the superiority of the proposed algorithm has been verified in extensive experiments.

Invertible color-to-grayscale conversion is a method that embeds the color information into the corresponding grayscale image and extracts the color information to reconstruct the color image when necessary. In this paper, we propose an efficient method by using K-means clustering to generate a color palette and its corresponding grayscale image, and further making use of a reversible watermarking technique to embed the color palette into the grayscale image. For purpose of integrality authentication, the grayscale image is hashed as part of the embedded information before the embedding. In the process of reconstructing the color image, the color palette can be extracted correctly and the grayscale image can be recovered without any loss. Experimental results have shown that the proposed method can provide satisfactory performance.

How to predict images is an important issue in the reversible data hiding (RDH) community. In this letter, we propose a novel CNN-based prediction approach by luminously dividing a grayscale image into two sets and applying one set to predict the other set for data embedding. The proposed CNN predictor is a lightweight and computation-efficient network with the capabilities of multi receptive fields and global optimization. This CNN predictor can be trained quickly and well by using 1000 images randomly selected from ImageNet. Furthermore, we propose a two stages of embedding scheme for this predictor. Experimental results show that the CNN predictor can make full use of more surrounding pixels to promote the prediction performance. Furthermore, in the experimental way we have shown that the CNN predictor with expansion embedding and histogram shifting techniques can provide better embedding performance in comparison with those classical linear predictors.

Cover-lossless robust watermarking is a new research issue in the information hiding community, which can restore the cover image completely in case of no attacks. Most countermeasures proposed in the literature usually focus on additive noise-like manipulations such as JPEG compression, low-pass filtering and Gaussian additive noise, but few are resistant to challenging geometric deformations such as rotation and scaling. The main reason is that in the existing cover-lossless robust watermarking algorithms, those exploited robust features are related to the pixel position. In this article, we present a new cover-lossless robust image watermarking method by efficiently embedding a watermark into low-order Zernike moments and reversibly hiding the distortion due to the robust watermark as the compensation information for restoration of the cover image. The amplitude of the exploited low-order Zernike moments are: 1) mathematically invariant to scaling the size of an image and rotation with any angle; and 2) robust to interpolation errors during geometric transformations, and those common image processing operations. To reduce the compensation information, the robust watermarking process is elaborately and luminously designed by using the quantized error, the watermarked error and the rounded error to represent the difference between the original and the robust watermarked image. As a result, a cover-lossless robust watermarking system against geometric deformations is achieved with good performance. Experimental results show that the proposed robust watermarking method can effectively reduce the compensation information, and the new cover-lossless robust watermarking system provides strong robustness to those content-preserving manipulations including scaling, rotation, JPEG compression and other noise-like manipulations. In case of no attacks, the cover image can be recovered without any loss.

Digital watermarking in encrypted domain is a potential technology for privacy protection (with encryption) and integrity authentication (with watermark) in cloud computing environments. Based on order‐preserving encryption scheme (OPES), discrete cosine transformation (DCT), cryptography hash and watermarking technologies, this study proposes a new database authentication watermarking scheme in encrypted domain. Firstly, data in a database are encrypted with OPES for privacy protection. Then, the encrypted data are divided into groups for DCT operations. The watermark bits generated by hashing AC coefficients are embedded into DC coefficients for integrity authentication of the encrypted data. In receiver, whether the data have been tampered can be claimed by matching the hash value of AC coefficients and the extracted watermark information from DC coefficients. The watermark embedding process in encrypted domain is lossless to plaintext data by exploring order‐preserving property of OPES. In the receiver, an illegal user can recover the original database by directly decrypting the watermarked ciphertext data. Experimental results have shown that the algorithm can efficiently detect different tampering operations while protecting data content security with OPES.

This paper proposes a novel reversible data hiding scheme for encrypted images by using homomorphic and probabilistic properties of Paillier cryptosystem. In the proposed method, groups of adjacent pixels are randomly selected, and reversibly embedded into the rest of the image to make room for data embedding. In each group, there are a reference pixel and a few host pixels. Least significant bits (LSBs) of the reference pixels are reset before encryption and the encrypted host pixels are replaced with the encrypted reference pixel in the same group to form mirroring ciphertext groups (MCGs). In such a way, the modification on MCGs for data embedding will not cause any pixel oversaturation in plaintext domain and the embedded data can be directly extracted from the encrypted domain. In an MCG, the reference ciphertext pixel is kept unchanged as a reference while data hider embeds the encrypted additional data into the LSBs of the host ciphertext pixels by employing homomorphic multiplication. On the receiver side, the hidden ciphertext data can be retrieved by employing a modular multiplicative inverse operation between the marked host ciphertext pixels and their corresponding reference ciphertext pixels, respectively. After that, the hidden data are extracted promptly by looking for a one-to-one mapping table from ciphertext to plaintext. Data extraction and image restoration can be accomplished without any error after decryption. Compared with the existing works, the proposed scheme has lower computation complexity, higher security performance, and better embedding performance. The experiments on the standard image files also certify the effectiveness of the proposed scheme.

Exposing digital audio forgeries in time domain is a significant research issue in the audio forensics community. In this paper, we develop an audio forensics method to detect and locate audio forgeries in time domain (including deletion, insertion, substitution and splicing) by analyzing singularity points of audio signals after performing discrete wavelet packet decomposition. Firstly, we observe and point out that a forgery operation in time domain will often generate a singularity point because the correlation property of those samples close to the tampering position has been degraded. Furthermore, we investigate and find that the singularity point resulted from a tampering operation often stays alone while those inherent singularity points in the original signal usually staying in the form of group. Finally, we propose an approach to expose audio forgeries in time domain by introducing Mallat et al.'s wavelet singularity analysis method and making a difference between a forged point and the inherent singularity points. Extensive experimental results have shown that the proposed scheme can better identify whether a given speech file has been tampered (e.g., part of the content deleted or replaced) previously and further locate the forged positions in time domain.

Watermark resistance to geometric attacks is an important issue in the image watermarking community. Most countermeasures proposed in the literature usually focus on the problem of global affine transforms such as rotation, scaling and translation (RST), but few are resistant to challenging cropping and random bending attacks (RBAs). The main reason is that in the existing watermarking algorithms, those exploited robust features are more or less related to the pixel position. In this paper, we present an image watermarking scheme by the use of two statistical features (the histogram shape and the mean) in the Gaussian filtered low-frequency component of images. The two features are: 1) mathematically invariant to scaling the size of images; 2) independent of the pixel position in the image plane; 3)statistically resistant to cropping; and 4) robust to interpolation errors during geometric transformations, and common image processing operations. As a result, the watermarking system provides a satisfactory performance for those content-preserving geometric deformations and image processing operations, including JPEG compression, lowpass filtering, cropping and RBAs.

In audio watermarking area, the robustness against desynchronization attacks, such as TSM (Time-Scale Modification) and random cropping operations, is still one of the most challenging issues. In this paper, we present a multibit robust audio watermarking solution for such a problem by using the insensitivity of the audio histogram shape and the modified mean to TSM and cropping operations. We address the insensitivity property in both mathematical analysis and experimental testing by representing the histogram shape as the relative relations in the number of samples among groups of three neighboring bins. By reassigning the number of samples in groups of three neighboring bins, the watermark sequence is successfully embedded. In the embedding process, the histogram is extracted from a selected amplitude range by referring to the mean in such a way that the watermark will be able to be resistant to amplitude scaling and avoid exhaustive search in the extraction process. The watermarked audio signal is perceptibly similar to the original one. Experimental results demonstrate that the hidden message is very robust to TSM and random cropping attacks, and also has a satisfactory robustness for those common audio signal processing operations.