nils-herrmann/clean_scirepeval huggingface dataset:
An efficient print-scanning resilient data hiding scheme based on a novel LPM

Print-scan resilient data hiding has not been extensively researched. This paper presents an efficient multi-bit blind watermarking scheme based on a novel Fourier log-polar mapping (LPM). The watermark resynchronization after print-scanning is efficiently solved by an embedded tracking pattern which cannot be removed by template removing attacks and is not detectable for a malicious part. Experimental results show that the proposed watermarking scheme has excellent robustness to print-scanning, cropping, geometric distortion and JPEG compression etc. The obtained success ratios of extraction 60 bits message without error from the combination attack of JPEG compressed with quality factor of 50-100 and then print- scanning were at least 95%.
Anti-forensics of JPEG Detectors via Adaptive Quantization Table Replacement

Due to the popularity of JPEG compression standard, JPEG images have been widely used in various applications. Nowadays, detection of JPEG forgeries becomes an important issue in digital image forensics, and lots of related works have been reported. However, most existing works mainly rely on a pre-trained classifier according to the quantization table shown in the file header of the suspicious JPEG image, and they assume that such a table is authentic. This assumption leaves a potential flaw for those wise forgers to confuse or even invalidate the current JPEG forensic detectors. Based on our analysis and experiments, we found that the generalization ability of most current JPEG forensic detectors is not very good. If the quantization table changes, their performances would decrease significantly. Based on this observation, we propose a universal anti-forensic scheme via replacing the quantization table adaptively. The extensive experimental results evaluated on 10,000 natural images have shown the effectiveness of the proposed scheme for confusing four typical JPEG forensic works.
IEEE Access Special Section Editorial: Latest Advances and Emerging Applications of Data Hiding

Data hiding is the science and art of imperceptibly embedding some secret data, e.g., marks or messages, into a host media. The secret data are detected and extracted at a receiving end for various purposes. Data hiding has received tremendous research efforts during the past decade from both academia and industry for a diverse range of applications such as media copyright protection, content authentication, digital rights management, media system monitoring, covert communications, and so on.
Universal JPEG steganalysis based on microscopic and macroscopic calibration

In this paper, we present a new universal steganalysis scheme to effectively attack some recently proposed JPEG steganography. Different from the other steganalyzers, not only the magnitude but also the sign dependencies existed in intra-block and inter-block quantized DCT (discrete cosine transform) coefficients are exploited by the Markov empirical transition matrices. Moreover, a new microscopic and macroscopic calibration method is proposed to calibrate the local and global distribution of the quantized DCT coefficients of the test image, thus improve the detecting performance. Experimental results demonstrate that our proposed scheme outperforms some existing steganalyzers in attacking the advanced JPEG steganography such as F5, MB1 and Outguess.
A blind audio watermarking algorithm with self-synchronization

A blind audio information bit hiding algorithm with effective synchronization is proposed in this paper. The algorithm embeds synchronization signals in the time domain to resist the attacks such as cropping while keeping the computation for resynchronization lower. The watermark is placed in blockwise DCT coefficients of the original audio exploiting the HAS (Human Audio System) features. To lower the bit error rate of the watermark in extraction, error-correcting coding is applied. The experimental results show that the hidden imperceptible watermark is robust to the attacks caused by additive noise, MP3 coding, and cropping.
Binary Image Authentication using Zernike Moments

In this paper, we propose a content-based binary image authentication scheme. At first, we use Zernike moments magnitudes (ZMM) to generate the feature vector and demonstrate that this feature vector can represent the binary image and decide its authenticity effectively. Then the watermark is generated by quantizing ZMMs and embedded into the image. The authentication doesn't need the original watermark. The decision depends on the distance between the extracted watermark and the feature vector of the test image and a metric measure. To decrease the influence of watermarking on the feature vector, we split the binary image into two parts by a random mask, one for generating feature vector and the other for embedding watermark. Zernike moments are usually computationally expensive, so we propose a fast algorithm. Extensive experiments show that our scheme can detect malicious attacks effectively.
Improve robustness of image watermarking via adaptive receiving

Almost all the existing popular watermarking schemes model the watermarking channel noises as additive noise with zero-mean. However, our experiments show that this is not reasonable in the case of channel noise introduced by image filtering. This paper presents a new quantization-based watermarking scheme with enhanced robustness via adaptive receiving and turbo coding in addition to other measures. The present algorithm can successfully resist almost all the StirMark testing functions including both common signal processing and geometric distortions in StirMark 4.0 except for random distortion.
Countering anti-JPEG compression forensics

The quantization artifacts and blocking artifacts are the two significant properties in the JPEG compressed images. Most relative forensic techniques usually use such inherent properties to provide some evidences on how image data is acquired and/or processed. A wise attacker, however, may perform some post-operations to confuse the two artifacts to fool current forensic techniques. Recently, Stamm et al. in  propose a novel anti-JPEG compression method via adding anti-forensic dither to the DCT coefficients and further reducing the blocking artifacts. In this paper, we found that the dithering operation will inevitably destroy the statistical correlations among the 8 × 8 intrablock and interblock within an image. In the view of JPEG steganalysis, we employ the transition probability matrix of the DCT coefficients to measure such modifications for identifying the forged images from those original JPEG decompressed images and uncompressed ones. On average, we can obtain a detection accuracy as high as 99% on the image database of UCID .
Edge Adaptive Image Steganography Based on LSB Matching Revisited

The least-significant-bit (LSB)-based approach is a popular type of steganographic algorithms in the spatial domain. However, we find that in most existing approaches, the choice of embedding positions within a cover image mainly depends on a pseudorandom number generator without considering the relationship between the image content itself and the size of the secret message. Thus the smooth/flat regions in the cover images will inevitably be contaminated after data hiding even at a low embedding rate, and this will lead to poor visual quality and low security based on our analysis and extensive experiments, especially for those images with many smooth regions. In this paper, we expand the LSB matching revisited image steganography and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image. For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameters. The experimental results evaluated on 6000 natural images with three specific and four universal steganalytic algorithms show that the new scheme can enhance the security significantly compared with typical LSB-based approaches as well as their edge adaptive ones, such as pixel-value-differencing-based approaches, while preserving higher visual quality of stego images at the same time.
Adaptive filtering of block effects based on edge map

Block coding, such as DCT coding and VQ, play an important role in image data compression. However, block coding usually produces block effects in the reconstructed images. Block effects result in the artificial contours and so degrade the quality of the decoded images. To improve the visual quality of decoded images, postprocessing is often used. We present an adaptive space-variant filter based edge map for reducing block effects. First we apply an edge detector on the decoded image to obtain an edge map. Then, an environment of each pixel (x,y) in the decoded image f(x,y) is analyzed based on the edge map. Based on the analysis, all pixels in f(x,y) are classified into two classes: edge pixels and non-edge pixels. Finally, a 1-D directional filter and a 2-D mean filter are applied on those pixels accordingly. Simulation results show that the algorithm improved the reconstructed images both in the subject visual quality and signal-to-noise ratio.
Detection of Quantization Artifacts and Its Applications to Transform Encoder Identification

Quantization is one of the commonly used techniques in most lossy image source encoders. It is observed that the quantization operation usually introduces some obvious artifacts into the histogram of the corresponding transform coefficients under various compression schemes. By investigating such inherent artifacts over all candidate transform coefficients, it is possible to identify the transform, as well as some parameters previously employed in the transform-based encoder from a decompressed image. In this paper, we first analyze the properties of the quantized coefficients and present a simple yet effective way to detect the quantization artifacts, and then we propose an approach to identify the transform-based encoder based on the quantization artifacts detection. The simulation results evaluated on thousands of natural images with some popular compression schemes demonstrate the effectiveness of our method.
Enhancing Source Camera Identification Performance With a Camera Reference Phase Sensor Pattern Noise

Sensor pattern noise (SPN) extracted from digital images has been proved to be a unique fingerprint of digital cameras. However, SPN can be contaminated largely in the frequency domain by image content and nonunique artefacts of JPEG compression, on-sensor signal transfer, sensor design, color interpolation. The source camera identification (CI) performance based on SPN needs to be improved for small sizes of images and especially in resisting JPEG compression. Because the SPN is modelled as an additive white Gaussian noise (AWGN) in its extraction process from an image, it is reasonable to assume the camera reference SPN to be a white noise signal in order to remove the interference mentioned above. The noise residues (SPN) extracted from the original images are whitened first, then they are averaged to generate the camera reference SPN. Motivated by Goljan 's test statistic peak to correlation energy (PCE), we propose to use correlation to circular correlation norm (CCN) as the test statistic, which can lower the false positive rate to be a half of that with PCE. Theoretical analysis shows that the proposed CI method can remove the interference and raise the CCN value of a positive sample and thus achieve greater CI performance, CCN values of the negative sample class with the proposed method follow the normal distribution N (0,1) and the false positive rate can be calculated. Compared with the existing state of the art on seven cameras, 1400 photos totally (200 for each camera), the experimental results show that the proposed CI method achieves the best receiver operating characteristic (ROC) performance among all CI methods in all cases and especially achieves much better resistance to JPEG compression than all of the existing state-of-the-art CI methods.
Secure JPEG steganography by LSB+ matching and multi-band embedding

A new steganographic algorithm is proposed for JPEG images by modifying the block DCT coefficients. Firstly, an embedding algorithm called LSB+ matching is generated to approximately preserve the marginal distribution of DCT coefficients. We further divide the DCT coefficients into four frequency bands, including the direct current (DC), low-frequency, middle-frequency, and high-frequency. Via matrix encoding, low data hiding rate and high embedding efficiency are achieved in high-frequency band, while the hiding rate is increased in the middle-frequency and DC bands, and highest in the low-frequency band. In addition, a coefficient selection strategy is employed to make the hidden message less detectable. The proposed algorithm is implemented on a set of 10000 images and tested with four steganalytic algorithms. The experimental results show that it outperforms the F5, nsF5 and LSB+ algorithms in terms of detection accuracy for the JPEG images at various levels of quality.
Scheduling algorithms for multiprocessor real-time systems

Real-time systems are subject to fundamental user requirements, such as correctness of the systems, taking into account timeliness and simultaneity, a high degree of predictability. This means that the scheduling algorithm is an important component of these systems. A scheduling algorithm is used to determine a schedule for a set of tasks so that the task's deadlines and resource requirements are satisfied. The potential speedup of applications has motivated the widespread use of multiprocessors in recent years. In this paper, recent results on multiprocessor real-time scheduling algorithms are reviewed. The review takes the form of an analysis of the problems presented by different application requirements and characteristics. A scheduling algorithm for real-time systems to be implemented on multiprocessors is proposed. This algorithm is appropriate for real-time tasks with the precedence constraints.
Identification of Various Image Operations Using Residual-Based Features

Image forensics has attracted wide attention during the past decade. However, most existing works aim at detecting a certain operation, which means that their proposed features usually depend on the investigated image operation and they consider only binary classification. This usually leads to misleading results if irrelevant features and/or classifiers are used. For instance, a JPEG decompressed image would be classified as an original or median filtered image if it was fed into a median filtering detector. Hence, it is important to develop forensic methods and universal features that can simultaneously identify multiple image operations. Based on extensive experiments and analysis, we find that any image operation, including existing anti-forensics operations, will inevitably modify a large number of pixel values in the original images. Thus, some common inherent statistics such as the correlations among adjacent pixels cannot be preserved well. To detect such modifications, we try to analyze the properties of local pixels within the image in the residual domain rather than the spatial domain considering the complexity of the image contents. Inspired by image steganalytic methods, we propose a very compact universal feature set and then design a multiclass classification scheme for identifying many common image operations. In our experiments, we tested the proposed features as well as several existing features on 11 typical image processing operations and four kinds of anti-forensic methods. The experimental results show that the proposed strategy significantly outperforms the existing forensic methods in terms of both effectiveness and universality.
Invariant Image Watermarking Based on Statistical Features in the Low-Frequency 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.
Detecting doubly compressed JPEG images by using Mode Based First Digit Features

In this paper, we utilize the probabilities of the first digits of quantized DCT (discrete cosine transform) coefficients from individual AC (alternate current) modes to detect doubly compressed JPEG images. Our proposed features, named by mode based first digit features (MBFDF), have been shown to outperform all previous methods on discriminating doubly compressed JPEG images from singly compressed JPEG images. Furthermore, combining the MBFDF with a multi-class classification strategy can be exploited to identify the quality factor in the primary JPEG compression, thus successfully revealing the double JPEG compression history of a given JPEG image.
Reliable information bit hiding

One of challenges encountered in information bit hiding is the reliability of information bit detection. This paper addresses the issue and presents an algorithm in the discrete cosing transform (DCT) domain with a communication theory approach. It embeds information bits (first) in the DC and (then in the) low-frequency AC coefficients. To extract the hidden information bits from a possibly corrupted marked image with a low error probability, we model information hiding as a digital communication problem and apply Bose?Chaudhuri?Hocquenghen channel coding with soft-decision decoding based on matched filtering. The robustness of the hidden bits has been tested with StirMark. The experimental results demonstrate that the embedded information bits are perceptually transparent and can successfully resist common signal processing procedures, jitter attack, aspect ratio variation, scaling change, small angle rotation, small amount cropping, and JPEG compression with quality factor as low as 10. Compared with some information hiding algorithms reported in the literature, it appears that the hidden information bits with the proposed approach are relatively more robust. While the approach is presented for gray level images, it can also be applied to color images and video sequences.
Efficiently self-synchronized audio watermarking for assured audio data transmission

In this paper, we propose a self-synchronization algorithm for audio watermarking to facilitate assured audio data transmission. The synchronization codes are embedded into audio with the informative data, thus the embedded data have the self-synchronization ability. To achieve robustness, we embed the synchronization codes and the hidden informative data into the low frequency coefficients in DWT (discrete wavelet transform) domain. By exploiting the time-frequency localization characteristics of DWT, the computational load in searching synchronization codes has been dramatically reduced, thus resolving the contending requirements between robustness of hidden data and efficiency of synchronization codes searching. The performance of the proposed scheme in terms of SNR (signal to noise ratio) and BER (bit error rate) is analyzed. An estimation formula that connects SNR with embedding strength has been provided to ensure the transparency of embedded data. BER under Gaussian noise corruption has been estimated to evaluate the performance of the proposed scheme. The experimental results are presented to demonstrate that the embedded data are robust against most common signal processing and attacks, such as Gaussian noise corruption, resampling, requantization, cropping, and MP3 compression.
Attack LSB Matching Steganography by Counting Alteration Rate of the Number of Neighbourhood Gray Levels

In this paper, we propose a new method for attacking the LSB (least significant bit) matching based steganography. Different from the LSB substitution, the least two or more significant bit-planes of the cover image would be changed during the embedding in LSB matching steganography and thus the pairs of values do not exist in stego image. In our proposed method, we get an image by combining the least two significant bit-planes and divide it into 3x3 overlapped subimages. The subimages are grouped into four types, i.e. T 1,T 2, T 3 and T 4 according to the count of gray levels. Via embedding a random sequence by LSB matching and then computing the alteration rate of the number of elements in T 1, we find that normally the alteration rate is higher in cover image than in the corresponding stego image. This new finding is used as the discrimination rule in our method. Experimental results demonstrate that the proposed algorithm is efficient to detect the LSB matching stegonagraphy on uncompressed gray scale images.
Multi-Scale Fusion for Improved Localization of Malicious Tampering in Digital Images

A sliding window-based analysis is a prevailing mechanism for tampering localization in passive image authentication. It uses existing forensic detectors, originally designed for a full-frame analysis, to obtain the detection scores for individual image regions. One of the main problems with a window-based analysis is its impractically low localization resolution stemming from the need to use relatively large analysis windows. While decreasing the window size can improve the localization resolution, the classification results tend to become unreliable due to insufficient statistics about the relevant forensic features. In this paper, we investigate a multi-scale analysis approach that fuses multiple candidate tampering maps, resulting from the analysis with different windows, to obtain a single, more reliable tampering map with better localization resolution. We propose three different techniques for multi-scale fusion, and verify their feasibility against various reference strategies. We consider a popular tampering scenario with mode-based first digit features to distinguish between singly and doubly compressed regions. Our results clearly indicate that the proposed fusion strategies can successfully combine the benefits of small-scale and large-scale analyses and improve the tampering localization performance.
A space and frequency hybrid algorithm to remove block artifacts

Postfiltering of block artifacts in decoded images has drawn extensive attention. But less improvements have been made for staircase noise. In this paper a space and frequency hybrid algorithm is presented. First, the edge information in the decoded images is extracted. Based on an analysis of artifacts in the frequency domain, the block effect in the edge image is removed by using a notch filter. The filtered edge image is used for a neighborhood analysis. A space-variant filter, consisting of a 2D LPF and a 1D directional LPF, is then applied to the decoded image to reduce artifacts. Which of the filters is chosen depends on the neighborhood analysis. Finally, the contrast is enhanced. The simulation results demonstrate that the proposed algorithm improves both the subjective visual quality and PSNR of decoded images effectively.