nils-herrmann/clean_scirepeval huggingface dataset:
A Strategy of Clustering Modification Directions in Spatial Image Steganography

Most of the recently proposed steganographic schemes are based on minimizing an additive distortion function defined as the sum of embedding costs for individual pixels. In such an approach, mutual embedding impacts are often ignored. In this paper, we present an approach that can exploit the interactions among embedding changes in order to reduce the risk of detection by steganalysis. It employs a novel strategy, called clustering modification directions (CMDs), based on the assumption that when embedding modifications in heavily textured regions are locally heading toward the same direction, the steganographic security might be improved. To implement the strategy, a cover image is decomposed into several subimages, in which message segments are embedded with well-known schemes using additive distortion functions. The costs of pixels are updated dynamically to take mutual embedding impacts into account. Specifically, when neighboring pixels are changed toward a positive/negative direction, the cost of the considered pixel is biased toward the same direction. Experimental results show that our proposed CMD strategy, incorporated into existing steganographic schemes, can effectively overcome the challenges posed by the modern steganalyzers with high-dimensional features.
Investigation on Cost Assignment in Spatial Image Steganography

Relating the embedding cost in a distortion function to statistical detectability is an open vital problem in modern steganography. In this paper, we take one step forward by formulating the process of cost assignment into two phases: 1) determining a priority profile and 2) specifying a cost-value distribution. We analytically show that the cost-value distribution determines the change rate of cover elements. Furthermore, when the cost-values are specified to follow a uniform distribution, the change rate has a linear relation with the payload, which is a rare property for content-adaptive steganography. In addition, we propose some rules for ranking the priority profile for spatial images. Following such rules, we propose a five-step cost assignment scheme. Previous steganographic schemes, such as HUGO, WOW, S-UNIWARD, and MG, can be integrated into our scheme. Experimental results demonstrate that the proposed scheme is capable of better resisting steganalysis equipped with high-dimensional rich model features.
Embedding image watermarks in dc components

Both watermark structure and embedding strategy affect robustness of image watermarks. Where should watermarks be embedded in the discrete cosine transform (DCT) domain in order for the invisible image watermarks to be robust? Though many papers in the literature agree that watermarks should be embedded in perceptually significant components, dc components are explicitly excluded from watermark embedding. In this letter, a new embedding strategy for watermarking is proposed based on a quantitative analysis on the magnitudes of DCT components of host images. We argue that more robustness can be achieved if watermarks are embedded in dc components since dc components have much larger perceptual capacity than any ac components. Based on this idea, an adaptive watermarking algorithm is presented. We incorporate the feature of texture masking and luminance masking of the human visual system into watermarking. Experimental results demonstrate that the invisible watermarks embedded with the proposed watermark algorithm are very robust.
Video Sequence Matching Based on the Invariance of Color Correlation

Video sequence matching aims to locate a query video clip in a video database. It plays an important role in reducing storage redundancy and detecting video copies for copyright protection. In this paper, we propose an effective method for video sequence matching based on the invariance of color correlation. The proposed method first splits each key-frame into nonoverlapping blocks. For each block, we sort the red, green, and blue color components according to their average intensities, and use the percentage of the color correlation to generate a frame feature with a small size. Finally, the resulting video feature is made up of the consecutive frame features, which is demonstrated to be robust against most typical video content-preserving operations, including geometric distortion, blurring, noise contamination, contrast enhancement, and strong re-encoding. The experimental results show that the proposed method outperforms the existing methods in the literature, as well as the method based on the traditional color histogram. Furthermore, the time and space complexity of our algorithm are both satisfactory, which are very important for many real-time applications.
An adaptive video watermarking algorithm

Robustness is one of the major issues of digital watermarking algorithm. An effective method to improve the robustness of the watermark is to embed the watermark adaptively based on the perceptual property and signal characteristics. In this paper, an adaptive video watermarking algorithm based on wavelet domain is proposed. According to the properties of the 2-D wavelet coefficients, the watermark is inserted into the low frequency subband coefficients to achieve better robustness. In order to improve the strength of the components of the watermark, we propose to classify the coefficients of the low frequency subband based on the motion of the object and the texture complexity of the content in the video sequence. According to the result of classification, the strength of the watermark component is adjusted adaptively. The experimental results show that the watermark just generated is robust to video degradation and distortions, e.g., those that result from additive Gaussian noise, MPEG-2 coding at high compress-ratio, temporal downsampling and spatial downsampling, while the transparency of watermark is guaranteed.
Steganalysis of JPEG steganography with complementary embedding strategy

Recently, a new high-performance JPEG steganography with a complementary embedding strategy (JPEG-CES) was presented. It can disable many specific steganalysers such as the Chi-square family and S family detectors, which have been used to attack J-Steg, JPHide, F5 and OutGuess successfully. In this work, a study on the security performance of JPEG-CES is reported. Our theoretical analysis demonstrates that in this algorithm, the number of the different quantised discrete cosine transform (qDCT) coefficients and the symmetry of the qDCT coefficient histogram both will be disturbed when the secret message is embedded. Moreover, the intrinsic sign and magnitude dependencies that existed in intra-block and inter-block qDCT coefficients will be disturbed too. Thus it may be detected by some modern universal steganalysers which can catch these disturbances. In this work, the authors have proposed two new steganalytic approaches. Through exploring the distortions that have been introduced into the qDCT coefficient histogram and the dependencies existed in the intra-block and inter-block sense, respectively, these two alternative steganalysers can detect JPEG-CES effectively. In addition, via merging the features of these two steganalysers, a more reliable classifier can be obtained.
Clustering Steganographic Modification Directions for Color Components

It is conventionally assumed that steganographic schemes for gray-scale images can be directly applied to color images by embedding messages independently in different color channels. However, the correlation among color channels may be disturbed and it is unclear how to preserve the channel correlation so as to increase empirical security. In this paper, we propose a strategy called CMD-C (clustering modification directions for color components). The basic idea of the strategy is to change different color components from the same pixel location towards a positive or negative direction consistently. To implement the strategy, we decompose an image into several sub-images in which segmented hidden message bits are successively embedded. The embedding costs of a sub-image are computed by considering the correlation both within and among color channels. Experimental results show that the proposed CMD-C strategy has made great improvement over conventional methods in resisting state-of-the-art steganalytic methods.
Embedding strategy for image watermarking in DCT domain

Where should invisible image watermarks be embedded in the DCT domain in order for the watermarks to be robust? Though many papers in the literature agree that watermarks should be embedded in perceptually significant components, DC components are explicitly excluded from watermark embedding. A new embedding strategy for watermarking is proposed based on a quantitative analysis on magnitude of DCT components. We argue that more robustness can be achieved if watermarks are embedded in DC components since DC components have a much larger perceptual capacity than any AC components. Based on this idea, a watermarking algorithm is presented. We incorporate the feature of texture masking of the human vision system into watermarking. Experimental results demonstrate that the invisible watermarks thus embedded are very robust.
Efficient Encrypted Images Filtering and Transform Coding With Walsh-Hadamard Transform and Parallelization

Since homomorphic encryption operations have high computational complexity, image applications based on homomorphic encryption are often time consuming, which makes them impractical. In this paper, we study efficient encrypted image applications with the encrypted domain Walsh-Hadamard transform (WHT) and parallel algorithms. We first present methods to implement real and complex WHTs in the encrypted domain. We then propose a parallel algorithm to improve the computational efficiency of the encrypted domain WHT. To compare the WHT with the discrete cosine transform (DCT), integer DCT, and Haar transform in the encrypted domain, we conduct theoretical analysis and experimental verification, which reveal that the encrypted domain WHT has the advantages of lower computational complexity and a shorter running time. Our analysis shows that the encrypted WHT can accommodate plaintext data of larger values and has better energy compaction ability on dithered images. We propose two encrypted image applications using the encrypted domain WHT. To accelerate the practical execution, we present two parallelization strategies for the proposed applications. The experimental results show that the speedup of the homomorphic encrypted image application exceeds 12.
Image authentication using content based watermark

We propose a content based semi-fragile watermarking algorithm for image authentication. Zernike moments of the lowpass wavelet band of the host image are chosen as features. Experimental results show that Zernike moments have semi-fragile characteristics, that is, it is possible to tell whether the processing on the image is malicious or not. In order to tell where the image is changed, we also propose a structured embedding method for the quantized Zernike moments in the DWT (discrete wavelet transform) domain. Experimental results show that this method can locate the malicious tamper locally, the unit detection region is a 16/spl times/16 block, while being highly robust to content preserved processing, such as JPEG compression, Q/spl ges/50, and noise. Experiments on Stirmark also show that JPEG50-100, scale or ratio can be classified as non-malicious attacks, while jitter and cropping are malicious attacks.
Steganalysis of JPEG2000 Lazy-Mode Steganography using the Hilbert-Huang Transform Based Sequential Analysis

In this paper, we present a steganalytic method to attack JPEG2000 lazy-mode steganography proposed by Su et al. The key element of the method is the Hilbert-Huang transform based analysis of the code-block noise variance sequences of stego images and non-stego noisy images. The Hilbert transform based characteristic vectors are constructed via empirical mode decomposition of the sequences and the support vector machine classifier is used in classification. Experimental results have demonstrated effectiveness of the proposed steganalytic method. According to our best knowledge, this method is the first successful attack of JPEG2000 lazy-mode steganography. And furthermore, the proposed method takes first step towards the application of Hilbert-Huang transform in steganalysis and proves its great advantage.
A high capacity distortion-free data hiding algorithm for palette image

Data hiding techniques have extensive applications and have attracted much attention. However, one common drawback of virtually all current data hiding methods is the fact that the host image is inevitably and permanently distorted by some small amount of noise due to data hiding itself Although the distortion is often quite small, it may not be acceptable for medical or military images. For these applications, the stego-image cannot have any distortion, a high capacity distortion-free data hiding algorithm for the palette image is proposed in this paper. By utilizing the color redundancy in the host image, the algorithm can embed large amount of information, which usually can reach more than 10% of size of the host image. Meanwhile, it can be guaranteed that no distortion is introduced to the host image during data hiding process.
New Framework for Reversible Data Hiding in Encrypted Domain

In the past more than one decade, hundreds of reversible data hiding (RDH) algorithms have been reported. Via exploring the correlation between the neighboring pixels (or coefficients), extra information can be embedded into the host image reversibly. However, these RDH algorithms cannot be accomplished in encrypted domain directly, since the correlation between the neighboring pixels will disappear after encryption. In order to accomplish RDH in encrypted domain, specific RDH schemes have been designed according to the encryption algorithm utilized. In this paper, we propose a new simple yet effective framework for RDH in encrypted domain. In the proposed framework, the pixels in a plain image are first divided into sub-blocks with the size of $m\times n$ . Then, with an encryption key, a key stream (a stream of random or pseudorandom bits/bytes that are combined with a plaintext message to produce the encrypted message) is generated, and the pixels in the same sub-block are encrypted with the same key stream byte. After the stream encryption, the encrypted $m\times n$ sub-blocks are randomly permutated with a permutation key. Since the correlation between the neighboring pixels in each sub-block can be well preserved in the encrypted domain, most of those previously proposed RDH schemes can be applied to the encrypted image directly. One of the main merits of the proposed framework is that the RDH scheme is independent of the image encryption algorithm. That is, the server manager (or channel administrator) does not need to design a new RDH scheme according to the encryption algorithm that has been conducted by the content owner; instead, he/she can accomplish the data hiding by applying the numerous RDH algorithms previously proposed to the encrypted domain directly.
Content-based authentication algorithm for binary images

This paper proposes a content-based authentication scheme for tampering detection and localization of binary images. The watermark is generated by the feature vector of the original binary image, and embedded back into the image by a structural method. The feature vector we used is Zernike moments. It can tell the degree of the tamper in the binary image. A distance between two feature vectors is defined. We authenticate image by the distance between the extracted watermark and the feature vector of the test image. Once the tampering is detected, we can locate the tampered areas by comparing different components of the distance. The experimental results show that the algorithm can locate the tamper effectively.
A GA-Based Joint Coding and Embedding Optimization for Robust and High Capacity Image Watermarking

A new informed image watermarking algorithm is presented in this paper, which can achieve the information rate of 1/64 bits/pixel with high robustness. Firstly, a LOT (local optimal test) detector based on HMM in wavelet domain is developed to tackle the issue that the exact strength for informed embedding is unknown to the receiver. Then based on the LOT detector, the dirty-paper code for informed coding is constructed and the metric for the robustness is defined accordingly. Unlike the previous approaches of informed watermarking which take the informed coding and embedding process separately, the proposed algorithm implements a joint coding and embedding optimization for high capacity and robust watermarking. The genetic algorithm (GA) is employed to optimize the robustness and distortion constraints simultaneously. Experimental results show that the proposed algorithm achieves significant improvements in performance against JPEG, gain attack, low-pass filtering and etc.
Identification of Electronic Disguised Voices

Since voice disguise has been an increasing tendency in illegal applications, and it has a great negative impact on establishing the authenticity of audio evidence for audio forensics, it is important to be able to identify whether a suspected voice has been disguised or not. However, few studies on such identification have been reported. In this paper, we propose an algorithm to identify electronic disguised voices. Since voice disguise, in essence, the modification of the frequency spectrum of speech signals, and mel-frequency cepstrum coefficients (MFCCs) can be used to well describe frequency spectral properties, MFCC-based features are supposed to be effective for the identification of disguised voices. In this paper, MFCC statistical moments including mean values and correlation coefficients are extracted as acoustic features. Then, an algorithm based on the extracted features and support vector machine classifiers is proposed to separate disguised voices from original voices. Extensive experiments show that the detection rates higher than 90% of the voices from various speech databases and disguised by various methods can be achieved, indicating that the identification performance of this algorithm is remarkable.
Steganalysis of YASS

A promising steganographic method-yet another steganography scheme (YASS)-was designed to resist blind steganalysis via embedding data in randomized locations. In addition to a concrete realization which is named the YASS algorithm in this paper, a few strategies were proposed to work with the YASS algorithm in order to enhance the data embedding rate and security. In this work, the YASS algorithm and these strategies, together referred to as YASS, have been analyzed from a warden's perspective. It is observed that the embedding locations chosen by YASS are not randomized enough and the YASS embedding scheme causes detectable artifacts. We present a steganalytic method to attack the YASS algorithm, which is facilitated by a specifically selected steganalytic observation domain (SO-domain), a term to define the domain from which steganalytic features are extracted. The proposed SO-domain is not exactly, but partially accesses, the domain where the YASS algorithm embeds data. Statistical features generated from the SO-domain have demonstrated high effectiveness in detecting the YASS algorithm and identifying some embedding parameters. In addition, we discuss how to defeat the above-mentioned strategies of YASS and demonstrate a countermeasure to a new case in which the randomness of the embedding locations is enhanced. The success of detecting YASS by the proposed method indicates a properly selected SO-domain is beneficial for steganalysis and confirms that the embedding locations are of great importance in designing a secure steganographic scheme.
Improved steganalysis algorithm against motion vector based video steganography

This paper proposes an improved steganalysis algorithm to detect the secret message hidden in the compressed video. As majority of video steganographic algorithms modify motion vectors (MV) in inter-frame encoding to hide data, aliasing effect may be caused in the distribution of the difference between MVs in two adjacent macroblocks. This phenomenon has been observed in detecting the secret data that were added to the MVs in cover video. To exploit the correlations between the neighboring MVs so as to detect the hidden data more efficiently, we consider the joint distribution of the MV differences between one macroblock and the other two macroblocks neighboring to it. The calculated joint probability mass functions are used to distinguish the stego videos from the non-stego ones. The experimental results show that significant improvement in detection accuracy can be made by using the joint distribution of MV differences instead of the statistics calculated from two neighboring MVs as features.
Band Energy Difference for Source Attribution in Audio Forensics

Digital audio recordings are one of the key types of evidence used in law enforcement proceedings. As a result, the development of reliable techniques for forensic analysis of such recordings is of principal importance. One of the main problems in forensic analysis is source attribution, i.e., verifying whether a certain recording was acquired with a given device. While this problem has been widely studied for other types of multimedia signals, there are a very few techniques for audio recordings. Moreover, reported evaluation results were obtained from extremely small data sets on the order of a dozen devices. The goal of this paper is to propose a new feature set, the band energy difference (BED) descriptor, for source attribution of digital speech recordings. We demonstrate that a frequency response curve extracted from sample recordings can serve as a robust fingerprint that carries significant discriminative power and can characterize the recording device. We study two sub-problems of source attribution: 1) identification of a recording device among a list of possible candidates (device identification) and 2) confirming that a suspected device has indeed been used to acquire the recording in question (device verification). For our evaluation, we prepared two novel data sets: a controlled-conditions data set with 31 devices and an uncontrolled-conditions data set with 141 devices. Our experimental evaluation demonstrates that the proposed BED descriptor is effective for both device identification and verification. In the former task, we reached an accuracy of over 96%. In the latter, we obtained a high true positive rate of 89% while maintaining a fixed low false positive rate of 1%.
Evaluation of random field models in multi-modal unsupervised tampering localization

While it is commonly known that successful forensic detectors should combine clues from various forensic features, unsupervised multi-modal tampering localization is still an open problem. State-of-the-art fusion methods perform simple pixel-wise combination of the input tampering maps. In this study, we show that pixel-wise combination is sub-optimal and successful fusion needs to model dependencies between neighboring pixels and exploit the content of the tampered image. We evaluate two methods based on conditional random fields and demonstrate that they can exploit image content and precisely delineate the shape of the forgery. In contrast to existing methods based on explicit image segmentation, such an approach does not suffer from subtle object removal forgeries where meaningful segments do not exist. We also demonstrate that existing performance measures are insufficient to accurately assess tampering localization performance. Further work in this direction is needed.