nils-herrmann/clean_scirepeval huggingface dataset:
Blind detection of electronic disguised voice

Since voice disguise has great negative impact on establishing authenticity of audio evidence in forensics, and has shown an increasing tendency in illegal applications, it is important to identify whether a suspected voice has been disguised or not. However, research on such detection has not been reported. In this paper, we focus on blind detection of electronic disguised voice. Statistical moments of Mel-frequency cepstrum coefficients (MFCC) are extracted as acoustic features of speech signals. Then an approach for detection of disguised voice based on the extracted features and Support Vector Machine (SVM) classifiers is proposed. The extensive experiments demonstrate that detection rates higher than 95% can be achieved, indicating that detection performance of the proposed approach is good.
Digital image forensics using statistical features and neural network classifier

Digital image forensics is a new topic in recent years, which deals with the authenticity and credibility of digital images. How to recognize fake images is still a problem. This paper presents a fake image classification scheme using higher order image statistics and RBF neural networks. The features constructed on the higher order statistics reveal the intrinsic statistical features between fake images and real images. Then a classifier based on RBF neural networks is used to classify the fake and real images using these features. Experimental results demonstrated the effectiveness of the proposed scheme.
Improve security of fragile watermarking via parameterized wavelet

The security is an important issue in watermarking. It has not, however, received enough attention yet. In this paper, we propose a secure fragile watermarking algorithm based on parameterized integer wavelet transform, and the rational range of the parameter is derived theoretically. Without the parameter of the wavelet base used for watermarking, it is hard for attacker to recover or attack the hidden watermark. Multiresolution tamper detection is developed for the accurate detection. Both security and lower computational complexity of the generated fragile watermark are achieved.
Reversible image watermarking by rhombus prediction and histogram modification

A histogram based reversible data hiding algorithm is proposed for digital images with the rhombus prediction scheme. With the prediction errors generated in each of the two phases, data embedding is performed by modifying their histogram while the overflow and underflow of pixel values are prevented in pre-process. Blind data extraction and exact recovery of the original image are both enabled by saving the overhead information into the watermarked image. Furthermore, high-capacity embedding can be performed by using multiple pairs of histogram bins to host the embedded data. The numerical results show that good performance can be achieved with the proposed algorithm, especially when the embedding rate is high.
Audio Postprocessing Detection Based on Amplitude Cooccurrence Vector Feature

Authentication of audio signals is an important problem in multimedia forensics. Tampering is typically followed by postprocessing that aims to remove the traces of the forgery. The variety of possible postprocessing operations makes tampering detection even more challenging. In this letter, we propose the amplitude cooccurrence vector features, which exploit cooccurrence patterns in audio signals. Experimental results show that our proposed features are able to distinguish between the original audio and the postprocessed audio with an average accuracy of above 95%. Furthermore, it can also effectively discriminate different kinds of postprocessing operations.
Efficient General Print-Scanning Resilient Data Hiding Based on Uniform Log-Polar Mapping

This paper proposes an efficient, blind, and robust data hiding scheme which is resilient to both geometric distortion and the general print-scan process, based on a near uniform log-polar mapping (ULPM). In contrast to performing inverse log-polar mapping (a mapping from the log-polar system to the Cartesian system) to the watermark signal or its index as done in the prior works, we apply ULPM to the frequency index (u, v) in the Cartesian system to obtain the discrete log-polar coordinate (l 1, l 2), then embed one watermark bit w(l 1 ,l 2 ) in the corresponding discrete Fourier transform coefficient c(u,v). This mapping of index from the Cartesian system to the log-polar system but embedding the corresponding watermark directly in the Cartesian domain not only completely removes the interpolation distortion and the interference distortion introduced to the watermark signal as observed in some prior works, but also largely expands the cardinality of watermark in the log-polar mapping domain. Both theoretical analysis and experimental results show that the proposed watermarking scheme achieves excellent robustness to geometric distortion, normal signal processing, and the general print-scan process. Compared to existing watermarking schemes, our algorithm offers significant improvement in terms of robustness against general print-scan, receiver operating characteristic (ROC) performance, and efficiency of blind resynchronization.
Forensic sensor pattern noise extraction from large image data set

The sensor pattern noise (SPN) can be regarded as the unique identity of a digital camera which is highly useful in digital image forensics [1, 2]. Existing methods [1, 2] which works by denoising each individual natural image often took an investigator a long time and great efforts to collect sufficient photos of diversified enough natural scenes. These processes are hard to repeat or standardized for officially using by an authority. In this work, we create noise image data set by taking photos of random noises displayed on a high definition monitor and propose a homomorphic based SPN extraction method. It offers the forensic researcher a fast way to create a large image data set in a few minutes. And the extraction method only needs to denoise once, which is highly efficient to deal with large numbers of photos. We compared the source camera identification performance of the proposed SPN extraction method to a prior state-of-art with identical experimental settings. The experimental results confirm the effectiveness of the proposed method.
A robust DWT-based video watermarking algorithm

In this paper, we propose a DWT-based video watermarking algorithm. It embeds multiple information bits into uncompressed video sequences. Its features include: i) embedding data in the LL subband; ii) reducing error probabilities of detection by exploiting the BCH code; iii) combating bursts of errors by using a novel 3-D interleaving technique; iv) developing an effective temporal synchronization technique (compared with the sliding correlation proposed by Hartung and Girod, our synchronization technique is more advance); and v) a valid secure key is needed in watermarking extraction. Experimental results demonstrate that the hidden data with the proposed algorithm is visually transparent, robust against MPEG-2 compression, scaling 2.0, and the detection error rate with 3-D interleaving is much lower than that without 3-D interleaving when frame loss rate is below 50%. The imperceptible high bit rate watermark thus embedded is robust and blind extracted. The experiments have demonstrated the effectiveness of the proposed algorithm.
A DWT-based image watermarking algorithm

In this paper, a new embedding strategy for DWT-based watermarking is proposed. Different from the existing watermarking schemes in which low frequency coefficients are explicitly excluded from watermark embedding, we claim that watermarks should be embedded in the low frequency subband firstly, and the remains should be embedded in high frequency subbands according to the significance of subbands. We also claim that different embedding formula should be applied on the low frequency subband and high frequency subbands respectively. Applying this strategy, an adaptive algorithm incorporating the feature of visual masking of human vision system into watermarking is proposed. In the algorithm, a novel method to classify wavelet blocks is presented. The experimental results demonstrate that the watermarks generated with the proposed algorithm are invisible and robust against noise and commonly used image processing techniques.
A robust DWT-based blind data hiding algorithm

The watermark embedding strategy is one of factors that affect the robustness of the watermark. In this paper, a new embedding strategy based on the discrete wavelet transform (DWT) is proposed. Different from the existing schemes based on the DWT which have explicitly excluded LL subband coefficients from watermark embedding, we claim that watermarks should be embedded in the LL subband first, and then in high frequency subbands according to the visual significance of the subbands. We have compared the performance of our new embedding strategy with that of the commonly used ones, confirming the superiority of the proposed embedding strategy. Based on this strategy, a robust oblivious watermarking algorithm in the DWT domain is presented. It takes advantage of error correcting coding (ECC) and 2D interleaving techniques to lower the detection error probability. By taking a carefully designed measure in watermark bit embedding, watermarked images achieve a 2dB improvement in PSNR. The experimental results show that the watermark embedded with the proposed algorithm is robust and invisible.
Image watermarking algorithm applying CDMA

In this paper, we propose a DWT-based image-watermarking algorithm applying DS-CDMA. The improved orthogonal Gold sequences sets generated via m-sequences are used to spread spectrum of the watermark. The CDMA encoded watermark is embedded into the DWT domain by modulating selected DWT coefficients of the image in a fashion similar to CDMA. Blind recovery of the embedded data is achieved by analyzing the DWT coefficients of the watermarked image and the auto-correlation of orthogonal codes. The proposed algorithm is compared with the existing watermarking schemes based on CDMA. The experimental results demonstrate that the watermark with the proposed algorithm is robust to JPEG compression, Gaussian noise and other typical attacks.
An integrated classifier in classified coding

Block coding is one of the most common schemes in image data compression. To improve the performance of block coding, a classification can be applied to the blocks prior to coding. This results in an adaptive block coding method, i.e., classified coding. In this paper, an integrated classifier taking the features of the human vision system (HVS) into account in classified coding is proposed. First, we present a classifier based on the local contrast sensitivity of the HVS. Compared with the commonly used local variance-based classifier (LVC), it possesses lower computational complexity while preserving almost the same performance. To improve upon the single-parameter classifiers' poor adaptivity, we, then, propose an integrated classifier which is composed of three independent classification units. It exhibits the complementarity of different classifiers. The simulation results demonstrate that the proposed classifier performs better than the LVC.
Histogram-Based Audio Watermarking Against Time-Scale Modification and Cropping Attacks

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.
Mixed-strategy Nash equilibrium in the camera source identification game

Although sensor pattern noise (SPN) is recognized as a reliable device fingerprint for camera source identification (CSI), this fingerprint could have been forged by anti-forensics. In order to evaluate the performance in the case of both forensic investigator and forger exist, we model this interplay as a camera source identification game. The mixed-strategy Nash equilibrium is introduced to solve this game. The Nash equilibrium receiver operating characteristic (ROC) curves are obtained experimentally. Through our analysis, we are able to determine under which case the CSI result is reliable.
Copy-move detection of audio recording with pitch similarity

The widespread availability of audio editing software has made it very easy to create forgeries without perceptual trace. Copy-move is one of popular audio forgeries. It is very important to identify audio recording with duplicated segments. However, copy-move detection in digital audio with sample by sample comparison is invalid due to post-processing after forgeries. In this paper we present a method based on pitch similarity to detect copy-move forgeries. We use a robust pitch tracking method to extract the pitch of every syllable and calculate the similarities of these pitch sequences. Then we can use the similarities to detect copy-move forgeries of digital audio recording. Experimental result shows that our method is feasible and efficient.
Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework

Adoption of deep learning in image steganalysis is still in its initial stage. In this paper, we propose a generic hybrid deep-learning framework for JPEG steganalysis incorporating the domain knowledge behind rich steganalytic models. Our proposed framework involves two main stages. The first stage is hand-crafted, corresponding to the convolution phase and the quantization and truncation phase of the rich models. The second stage is a compound deep-neural network containing multiple deep subnets, in which the model parameters are learned in the training procedure. We provided experimental evidence and theoretical reflections to argue that the introduction of threshold quantizers, though disabling the gradient-descent-based learning of the bottom convolution phase, is indeed cost-effective. We have conducted extensive experiments on a large-scale data set extracted from ImageNet. The primary data set used in our experiments contains 500 000 cover images, while our largest data set contains five million cover images. Our experiments show that the integration of quantization and truncation into deep-learning steganalyzers do boost the detection performance by a clear margin. Furthermore, we demonstrate that our framework is insensitive to JPEG blocking artifact alterations, and the learned model can be easily transferred to a different attacking target and even a different data set. These properties are of critical importance in practical applications.
Localization of Diffusion-Based Inpainting in Digital Images

Image inpainting, an image processing technique for restoring missing or damaged image regions, can be utilized by forgers for removing objects in digital images. Since no obviously perceptible artifacts are left after inpainting, it is necessary to develop methods for detecting the presence of inpainting. In general, there are two main categories of image inpainting techniques: exemplar-based and diffusion-based techniques. Although several methods have been proposed for detecting exemplar-based inpainting, there is still no effective method for detecting diffusion-based inpainting. Usually, the tampered regions manipulated by diffusion-based inpainting techniques are much smaller than those manipulated by exemplar-based ones, presenting more challenges in detecting these regions. As a pioneering attempt, this paper proposes a method for the localization of diffusion-based inpainted regions in digital images. We first analyze the diffusion process in inpainting, and observe that the changes in the image Laplacian along the direction perpendicular to the gradient are different in the inpainted and untouched regions. Following this observation, we construct a feature set based on the intra-channel and inter-channel local variances of the changes to identify the inpainted regions. Finally, two effective post-processing operations are designed for further refining of the localization result. The extensive experimental results evaluated on both synthetic and realistic inpainted images show the effectiveness of the proposed method.
The M-band wavelets in image watermarking

Multi-band (M-band) wavelet domain presents a novelty to host the watermark. In this paper, a new family of M-band wavelets, which is symmetric and parameterized with a variable /spl lambda/, is proposed and applied to image watermarking. The parameter /spl lambda/ also can be used as a key in watermark detection to improve the security of watermark. The multi-resolution analysis (MRA) of M-band wavelet transform, integrating with the CDMA (code division multiple access) encoding techniques is studied and employed to watermarking. The security, imperceptibility, and the robustness against JPEG compression and Gaussian noise, are analyzed for the proposed watermarking scheme. The experiments of watermarking based on M-band wavelet transform provide more encouraging results than those based on 2-band wavelets.
Performance Enhancement for DWT-HMM Image Watermarking with Content-Adaptive Approach

A DWT-HMM (hidden Markov model in wavelet domain) image watermarking algorithm with content-adaptive approach is proposed in this paper to optimized the trade-off between robustness and visual quality, which is characterized as follows: the entropy mask proposed by Watson is constructed in wavelet domain; the entropy mask and the new developed integrated HVS are used as the measures to adaptively select image components for watermarking; repeat-accumulation (RA) code with erasure and error correction is employed to synchronize the watermarked image; and a posterior HMM is utilized in watermark detection. Considerable improvement in robustness performance with the proposed adaptive algorithm is obtained over the previous DWT-HMM watermarking algorithm with stochastic embedding.
Exposing Fake Bit Rate Videos and Estimating Original Bit Rates

Bit rate is one of the important criterions for digital video quality. With some video tools, however, video bit rate can be easily increased without improving the video quality at all. In such a case, a claimed high bit rate video would actually have poor visual quality if it is up-converted from an original lower bit rate version. Therefore, exposing fake bit rate videos becomes an important issue for digital video forensics. To the best of our knowledge, although some methods have been proposed for exposing fake bit rate MPEG-2 videos, no relative work has been reported to further estimate their original bit rates. In this paper, we first analyze the statistical artifacts of these fake bit rate videos, including the requantization artifacts based on the first-digit law in the DCT frequency domain (12-D) and the changes of the structural similarity indexes between the query video and its sequential bit rate down-converted versions in the spatial domain (4-D), and then we propose a compact yet very effective 16-D feature vector for exposing fake bit rate videos and further estimating their original bit rates. The extensive experiments evaluated on hundreds of video sequences with four different resolutions and two typical compression schemes (i.e., MPEG-2 and H.264/AVC) have shown the effectiveness of the proposed method compared with the existing relative ones.
A DWT-DFT composite watermarking scheme robust to both affine transform and JPEG compression

Robustness is a crucially important issue in watermarking. Robustness against geometric distortion and JPEG compression at the same time with blind extraction remains especially challenging. A blind discrete wavelet transform-discrete Fourier transform (DWT-DFT) composite image watermarking algorithm that is robust against both affine transformation and JPEG compression is proposed. The algorithm improves robustness by using a new embedding strategy, watermark structure, 2D interleaving, and synchronization technique. A spread-spectrum-based informative watermark with a training sequence is embedded in the coefficients of the LL subband in the DWT domain while a template is embedded in the middle frequency components in the DFT domain. In watermark extraction, we first detect the template in a possibly corrupted watermarked image to obtain the parameters of an affine transform and convert the image back to its original shape. Then, we perform translation registration using the training sequence embedded in the DWT domain, and, finally, extract the informative watermark. Experimental work demonstrates that the proposed algorithm generates a more robust watermark than other reported watermarking algorithms. Specifically it is robust simultaneously against almost all affine transform related testing functions in StirMark 3.1 and JPEG compression with quality factor as low as 10. While the approach is presented for gray-level images, it can also be applied to color images and video sequences.
An Experimental Study on the Security Performance of YASS

This paper presents an experimental study on the security performance of Yet Another Steganographic Scheme (YASS). It reports: 1) YASS's security performance with different input images, i.e., uncompressed images and JPEG compressed images; 2) YASS's security performance compared with two other JPEG steganographic schemes MB1 and F5; and 3) some experimental results about extended YASS.
Self-synchronized audio watermark in DWT domain

Robust audio watermarks present some challenges. In this paper, we propose a DWT-based audio watermarking algorithm to resist shifting and cropping. Synchronization codes are embedded into audio with informative watermark, and thus the watermark generated has the ability of self-synchronization. To achieve good robustness performance, we embed the synchronization codes and the watermark into the low-frequency coefficients in DWT domain. By exploring the time-frequency localization capability of DWT to decrease the computation load for searching synchronization codes effectively, we resolve the conflict between robustness and computation complexity of synchronization code. In addition, we analyze the performance of the proposed algorithm in terms of data payload, error probability of synchronization codes, SNR and BER. The experimental results show the watermark is robust against common signal processing manipulations and attacks, such as Gaussian noise corruption, requantization, cropping and MP3 compression.