nils-herrmann/clean_scirepeval huggingface dataset:
A geometrically resilient robust image watermarking scheme using deformable multi-scale transform

The robust performance against geometrical manipulations is still one of major concerns in robust watermarking although significant improvement has been achieved in past decades. In this paper, we tackle the global geometrical attacks by designing a deformable multi-scale transform (DMST) that has joint shiftability in position, orientation, and scale. Via DMST, we both derive theoretically the principles for geometrical synchronization and develop a template-based scheme to efficiently estimate geometrical parameters. Also, the hidden Markov model in the standard wavelet domain is extended to the steerable wavelet domain and further used to improve the performance of watermark extraction. Experimental simulation demonstrates that the proposed watermarking scheme is quite robust to the common signal processing, geometrical attacks, and their joint attacks.
Adaptive Steganalysis Based on Embedding Probabilities of Pixels

In modern steganography, embedding modifications are highly concentrated on the textural regions within an image, as such regions are difficult to model for steganalysis. Previous studies have shown that compared with non-adaptive strategies, this content adaptive strategy achieves stronger security against existing steganalysis. Based on the experiments and analyses, however, we found that this embedding property would inevitably lead to a large limitation in existing adaptive steganography. That is, it is possible for steganalyzers to estimate the regions that have probably been modified after data hiding. In this paper, we propose an adaptive steganalytic scheme based on embedding probabilities of pixels. The main idea of our scheme is that we assign different weights to different pixels in feature extraction. For those pixels with high embedding probabilities, their corresponding weights are larger, since they should contribute more to steganalysis and vice versa. By doing so, we can concentrate our attention on the regions that have probably been modified and significantly reduce the impact of other unchanged smooth regions. It is expected that our proposed method is an improvement on the existing steganalytic methods, which usually assume every pixel has the same contribution to steganalysis. The extensive experiments evaluated on four typical adaptive steganographic methods have shown the effectiveness of the proposed scheme, especially for low embedding rates, for example, lower than 0.20 bpp.
Pixel-Decimation-Assisted Steganalysis of Synchronize-Embedding-Changes Steganography

This paper deals with the state-of-the-art synchronize-embedding-changes (SECs) steganography. We propose the pixel-decimation-assisted steganalytic feature set, a novel feature set construction protocol that extends upon the recent selection-channel-aware spatial rich model maxSRMd2. Our method is based on pixel decimation, a specific type of image downsampling. Based on theoretical analysis and empirical evaluation, we clearly demonstrate that our method impairs the synchronization of embedding changes in SEC steganography, and improves the accuracy of embedding change probability estimation. Our method significantly improves stego image detection performance when extended from a selection-channel-aware rich-model feature set (maxSRMd2) and is robust to different image downsampling methods. Furthermore, increasing the number of sweeps in SEC steganography has no effect to the performance of our proposed method even though it further strengthens synchronization of embedding changes. It is worth noting that with ensemble classifier, the above-mentioned performance improvements are achieved at a little extra cost.
Improved Tampering Localization in Digital Image Forensics Based on Maximal Entropy Random Walk

In this paper we propose to use maximal entropy random walk on a graph for tampering localization in digital image forensics. Our approach serves as an additional post-processing step after conventional sliding-window analysis with a forensic detector. Strong localization property of this random walk will highlight important regions and attenuate the background - even for noisy response maps. Our evaluation shows that the proposed method can significantly outperform both the commonly used threshold-based decision, and the recently proposed optimization-based approach with a Markovian prior.
JPEG Image Encryption With Improved Format Compatibility and File Size Preservation

Image encryption techniques can be used to ensure the security and privacy of valuable images. The related works in this field have focused more on raster images than on compressed images. Many existing JPEG image encryption schemes are not quite well compatible with the JPEG standard, or the file size of an encrypted JPEG image is apparently increased. In this paper, a novel bitstream-based JPEG image encryption method is presented. First, the groups of successive DC codes that encode the quantized DC coefficient differences with the same sign are permuted within each group. Second, the left half and the right half of a group, whose size will increase with the number of iterations, of consecutive DC codes may be swapped with each other, depending on whether an overflow of quantized DC coefficients occurs during decoding. Third, all AC codes are classified into 63 categories according to their zero-run lengths, then the AC codes within each category are, respectively, scrambled. Finally, all MCUs, except for DC codes, are randomly shuffled as a whole. Moreover, an image-content-related encryption key is employed to provide further security. The experimental results show that the file size of an encrypted JPEG image is almost the same as that of the corresponding plaintext image except for slight variations because of byte alignment. In addition, the quantized DC coefficients decoded from an encrypted JPEG image will not fall outside the valid range. Improved format compatibility is provided compared with other related methods. Moreover, it is unnecessary to perform entropy encoding again because all of the encryption operations are performed directly on the JPEG bitstream. The proposed method proves to be secure against brute-force attacks, differential cryptanalysis, known plaintext attacks, and outline attacks. Our proposed method can also be applied to color JPEG images.
Audio recapture detection using deep learning

Since the audio recapture can be used to assist audio splicing, it is important to identify whether a suspected audio recording is recaptured or not. However, few works on such detection have been reported. In this paper, we propose an method to detect the recaptured audio based on deep learning and we investigate two deep learning techniques, i.e., neural network with dropout method and stack auto-encoders (SAE). The waveform samples of audio frame is directly used as the input for the deep neural network. The experimental results show that error rate around 7.5% can be achieved, which indicates that our proposed method can successfully discriminate recaptured audio and original audio.
A Digital Watermarking Scheme Based on DCT and SVD

This paper presents a digital watermarking scheme based on the discrete cosine transform (DCT) and singular value decomposition (SVD). In the proposed method, selected mid-band DCT coefficients from 8x8 block DCT are selected to form blocks which are later decomposed into three matrices, i.e. U, S and V. Finally the original watermark is also partitioned into blocks and then embedded into the matrix S. Experimental result shows that the presented scheme is robust to several kinds of attacks.
An Informed Watermarking Scheme Using Hidden Markov Model in the Wavelet Domain

Achieving robustness, imperceptibility and high capacity simultaneously is of great importance in digital watermarking. This paper presents a new informed image watermarking scheme with high robustness and simplified complexity at an information rate of 1/64 bit/pixel. Firstly, a Taylor series approximated locally optimum test (TLOT) detector based on the hidden Markov model (HMM) in the wavelet domain is developed to tackle the problem of unavailability of exact embedding strength in the receiver due to informed embedding. Then based on the TLOT detector and the concept of dirty-paper code design, new HMM-based spherical codes are constructed to provide an effective tradeoff between robustness and distortion. The process of informed embedding is formulated as an optimization problem under the robustness and distortion constraints and the genetic algorithm (GA) is then employed to solve this problem. Moreover, the perceptual distance in the wavelet domain is also developed and incorporated into the GA-based optimization. Simulation results demonstrate that the proposed informed watermarking algorithm has high robustness against common attacks in signal processing and shows a comparable performance to the state-of-the-art scheme with a greatly reduced arithmetic complexity.
Image Forgery Localization via Integrating Tampering Possibility Maps

Over the past decade, many efforts have been made in passive image forensics. Although it is able to detect tampered images at high accuracies based on some carefully designed mechanisms, localization of the tampered regions in a fake image still presents many challenges, especially when the type of tampering operation is unknown. Some researchers have realized that it is necessary to integrate different forensic approaches in order to obtain better localization performance. However, several important issues have not been comprehensively studied, for example, how to select and improve/readjust proper forensic approaches, and how to fuse the detection results of different forensic approaches to obtain good localization results. In this paper, we propose a framework to improve the performance of forgery localization via integrating tampering possibility maps. In the proposed framework, we first select and improve two existing forensic approaches, i.e., statistical feature-based detector and copy-move forgery detector, and then adjust their results to obtain tampering possibility maps. After investigating the properties of possibility maps and comparing various fusion schemes, we finally propose a simple yet very effective strategy to integrate the tampering possibility maps to obtain the final localization results. The extensive experiments show that the two improved approaches used in our framework significantly outperform the state-of-the-art techniques, and the proposed fusion results achieve the best  $\mathrm {F_{1}}$ -score in the IEEE IFS-TC Image Forensics Challenge.
Embedding gray level images

Hiding a gray scale image encounters some difficulties. In this paper, a DCT-based image hiding algorithm is proposed. The bit rate of the hidden image is reduced by applying an image compression technique. To extract the hidden data from the corrupted watermarked image, BCH coding and matched filtering are applied. With the proposed algorithm, we hide a gray scale image of 64/spl times/64/spl times/8 in an image of 512/spl times/512/spl times/8. The experimental results demonstrated that the embedded image can be extracted reliably from noisy watermarked images.
Robust Image Watermarking Based on Multiband Wavelets and Empirical Mode Decomposition

In this paper, we propose a blind image watermarking algorithm based on the multiband wavelet transformation and the empirical mode decomposition. Unlike the watermark algorithms based on the traditional two-band wavelet transform, where the watermark bits are embedded directly on the wavelet coefficients, in the proposed scheme, we embed the watermark bits in the mean trend of some middle-frequency subimages in the wavelet domain. We further select appropriate dilation factor and filters in the multiband wavelet transform to achieve better performance in terms of perceptually invisibility and the robustness of the watermark. The experimental results show that the proposed blind watermarking scheme is robust against JPEG compression, Gaussian noise, salt and pepper noise, median filtering, and Con-vFilter attacks. The comparison analysis demonstrate that our scheme has better performance than the watermarking schemes reported recently.
Shot Boundary Detection: An Information Saliency Approach

This paper proposes a new approach for shot boundary detection using information saliency. Both temporal and spatial saliency are considered to generate an information saliency map (ISM). The shot detection (both abrupt changes and gradual transitions) is then based on the change of saliency, Six publicly available video databases are used for evaluation and the results are encouraging. The overall performance of the proposed method outperforms two commercial software, namely VideoAnnex and VCM.
An algorithm for removable visible watermarking

A visible watermark may convey ownership information that identifies the originator of image and video. A potential application scenario for visible watermarks was proposed by IBM where an image is originally embedded with a visible watermark before posting on the web for free observation and download. The watermarked image which serves as a "teaser." The watermark can be removed to recreate the unmarked image by request of interested buyers. Before we can design an algorithm for satisfying this application, three basic problems should be solved. First, we need to find a strategy suitable for producing large amount of visually same but numerically different watermarked versions of the image for different users. Second, the algorithm should let the embedding parameters reachable for any legal user to make the embedding process invertible. Third, an unauthorized user should be prevented from removing the embedded watermark pattern. In this letter, we propose a user-key-dependent removable visible watermarking system (RVWS). The user key structure decides both the embedded subset of watermark and the host information adopted for adaptive embedding. The neighbor-dependent embedder adjusts the marking strength to host features and makes unauthorized removal very difficult. With correct user keys, watermark removal can be accomplished in "informed detection" and the high quality unmarked image can be restored. In contrast, unauthorized operation either overly or insufficiently removes the watermark due to wrong estimation of embedding parameters, and thus, the resulting image has apparent defect.
Detecting double compressed AMR audio using deep learning

The Adaptive Multi-Rate (AMR) audio codec is a widely used audio data compression scheme optimized for speech and adopted by many devices. With the audio editing software, it is easy to perform tampering on digital speech recording, which makes the audio forensics become an important and urgent issue. Usually, the tampered AMR audio is double compressed AMR audio. In this paper, we proposed a method to detect the double compressed AMR audio. Such technique may be served as a tool for authenticating the originality of audio recordings and detecting the forgery positions. Our proposed method is based on deep learning algorithm and a majority voting strategy is designed for decision. The experimental results show that our method is effective to detect the double compressed AMR audio. Besides, the potential application of this technique is also discussed.
2-D and 3-D successive packing interleaving techniques and their applications to image and video data hiding

This paper first introduces the recently developed 2-D and 3-D successive packing (SP) interleaving techniques. Then, initial investigation of applying the 2-D SP interleaving technique to still image data hiding and 3-D SP interleaving technique to video data hiding is presented. Simulations have demonstrated that the SP interleaving technique can spread error bursts effectively so that the error bursts can be corrected with a simple random error correction codes. As a result of interleaving, the robustness of hidden data can be significantly enhanced.
Detection of Double Compressed AMR Audio Using Stacked Autoencoder

The adaptive multi-rate (AMR) audio codec adopted by many portable recording devices is widely used in speech compression. The use of AMR speech recordings as evidence in court is growing. Nowadays, it is easy to tamper with digital speech recordings, which makes audio forensics increasingly important. The detection of double compressed audio is one of the key issues in audio forensics. In this paper, we propose a framework for detecting double compressed AMR audio based on the stacked autoencoder (SAE) network and the universal background model-Gaussian mixture model (UBM-GMM). Instead of hand-crafted features, we used the SAE to learn the optimal features automatically from the audio waveforms. Audio frames are used as network input and the last hidden layer's output constitutes the features of a single frame. For an audio clip with many frames, the features of all the frames are aggregated and classified by UBM-GMM. Experimental results show that our method is effective in distinguishing single/double compressed AMR audio and outperforms the existing methods by achieving a detection accuracy of 98% on the TIMIT database. Exhaustive experiments demonstrate the effectiveness and robustness of the proposed method.
Automatic Detection of Object-Based Forgery in Advanced Video

Passive multimedia forensics has become an active topic in recent years. However, less attention has been paid to video forensics. Research on video forensics, and especially on automatic detection of object-based video forgery, is still in its infancy. In this paper, we develop an approach for automatic identification and forged segment localization of object-based forged video encoded with advanced frameworks. The proposed approach starts with a frame manipulation detector. An automatic algorithm is proposed to identify object-based video forgery based on the frame manipulation detector. Then, a two-stage automatic algorithm is provided to accurately locate the forged video segments in the suspicious video. To construct the proposed frame manipulation detector, motion residuals are generated from the target video frame sequence. We regard the object-based forgery in video frames as image tampering in the motion residuals and employ the feature extractors that are originally built for still image steganalysis to extract forensic features from the motion residuals. The experiments show that the proposed approach achieves excellent results in both forged video identification and automatic forged temporal segment localization.
Using invisible watermarks to protect visibly watermarked images

Physical visible watermarks have been widely used for centuries. Now digital visible watermarks such as electronic logos find their applications in digital library, video broadcasting, and other multimedia services. Several visible watermarking techniques have been proposed in the literature, and meanwhile, some problems with visible watermarks are also under investigation. Among these problems, watermark removal and unauthorized insertion are two major concerns. We propose using an invisible watermark in visibly watermarked images to overcome these problems. When a visibly watermarked image is in question, the invisible watermark can provide appropriate ownership information. We first investigate what kind of invisible watermark is needed, and then, focus on the details of the invisible watermarking technique. The experiments have shown that the proposed algorithm can provide a very effective protection for visibly watermarked images.
A Hybrid Watermarking Scheme for Video Authentication

In this paper, we present a hybrid watermarking scheme for video authentication based on wavelet domain. It embeds one robust watermark for temporal authentication distinguishing different inter-attacks, such as frame loss, inserting and reordering. Other two watermarks are used for intra authentication, one for discriminating the malicious attacks from acceptable manipulations, and the other for locating malicious attacks. The latter two watermarks are content-based. Zernike moments magnitudes (ZMMs) of the lowpass wavelet band of the host video frames are chosen as features. Experimental results show that ZMMs are robust to MPEG-2 compression and slight noise, while fragile to content change. This semi-fragile property is used to tell malicious attacks from non-malicious attacks. We also use structure of the embedded ZMMs to locate the tampered area. Experimental results show that this scheme can authenticate the video effectively.
Detecting Double JPEG Compression With the Same Quantization Matrix

Detection of double joint photographic experts group (JPEG) compression is of great significance in the field of digital forensics. Some successful approaches have been presented for detecting double JPEG compression when the primary compression and the secondary compression have different quantization matrixes. However, when the primary compression and the secondary compression have the same quantization matrix, no detection method has been reported yet. In this paper, we present a method which can detect double JPEG compression with the same quantization matrix. Our algorithm is based on the observation that in the process of recompressing a JPEG image with the same quantization matrix over and over again, the number of different JPEG coefficients, i.e., the quantized discrete cosine transform coefficients between the sequential two versions will monotonically decrease in general. For example, the number of different JPEG coefficients between the singly and doubly compressed images is generally larger than the number of different JPEG coefficients between the corresponding doubly and triply compressed images. Via a novel random perturbation strategy implemented on the JPEG coefficients of the recompressed test image, we can find a “proper” randomly perturbed ratio. For different images, this universal “proper” ratio will generate a dynamically changed threshold, which can be utilized to discriminate the singly compressed image and doubly compressed image. Furthermore, our method has the potential to detect triple JPEG compression, four times JPEG compression, etc.
Security Analysis on Spatial  $\pm$1 Steganography for JPEG Decompressed Images

Although many existing steganalysis works have shown that the spatial ±1 steganography on JPEG pre-compressed images is relatively easier to be detected compared with that on the never-compressed images, most experimental results seem not very convincing since these methods usually assume that the quantization table of the JPEG stegos previously used is known before detection and/or the length of embedded message is fixed. Furthermore, there are just few effective quantitative algorithms for further estimating the spatial modifications. In this letter, we firstly propose an effective method to detect the quantization table from the contaminated digital images which are originally stored as JPEG format based on our recently developed work about JPEG compression error analysis , and then we present a quantitative method to reliably estimate the length of spatial modifications in those gray-scale JPEG stegos by using data fitting technology. The extensive experimental results show that our estimators are very effective, and the order of magnitude of prediction error can remain around measured by the mean absolute difference.