nils-herrmann/clean_scirepeval huggingface dataset:
Automatic detection, extraction and analysis of unrestrained gait using a wearable sensor system

Within this paper we demonstrate the effectiveness of a novel body-worn gait monitoring and analysis framework to both accurately and automatically assess gait during `free-living' conditions. Key features of the system include the ability to automatically identify individual steps within specific gait conditions, and the implementation of continuous waveform analysis within an automated system for the generation of temporally normalized data and their statistical comparison across subjects.
A Framework for Evaluating Stereo-Based Pedestrian Detection Techniques

Automated pedestrian detection, counting, and tracking have received significant attention in the computer vision community of late. As such, a variety of techniques have been investigated using both traditional 2-D computer vision techniques and, more recently, 3-D stereo information. However, to date, a quantitative assessment of the performance of stereo-based pedestrian detection has been problematic, mainly due to the lack of standard stereo-based test data and an agreed methodology for carrying out the evaluation. This has forced researchers into making subjective comparisons between competing approaches. In this paper, we propose a framework for the quantitative evaluation of a short-baseline stereo-based pedestrian detection system. We provide freely available synthetic and real-world test data and recommend a set of evaluation metrics. This allows researchers to benchmark systems, not only with respect to other stereo-based approaches, but also with more traditional 2-D approaches. In order to illustrate its usefulness, we demonstrate the application of this framework to evaluate our own recently proposed technique for pedestrian detection and tracking.
IDENTIFYING PERSON RE-OCCURRENCES FOR PERSONAL PHOTO MANAGEMENT APPLICATIONS

Automatic identification of "who" is present in individual digital images within a photo management system using only content-based analysis is an extremely difficult problem. The authors present a system which enables identification of person reoccurrences within a personal photo management application by combining image content-based analysis tools with context data from image capture. This combined system employs automatic face detection and body-patch matching techniques, which collectively facilitate identifying person re-occurrences within images grouped into events based on context data. The authors introduce a face detection approach combining a histogram-based skin detection model and a modified BDF face detection method to detect multiple frontal faces in colour images. Corresponding body patches are then automatically segmented relative to the size, location and orientation of the detected faces in the image. The authors investigate the suitability of using different colour descriptors, including MPEG-7 colour descriptors, color coherent vectors (CCV) and color correlograms for effective body-patch matching. The system has been successfully integrated into the MediAssist platform, a prototype Web-based system for personal photo management, and runs on over 13000 personal photos.
Multimodal Identification of Journeys

Due to the ubiquity of localisation technology, users now have the ability to keep a record of their own location, as a kind of ‘location diary’. Such a large collection of data can become unmanageable without some way to structure that data to make it useful and searchable. We address this problem of structuring location data by proposing a framework for classifying the data into often-traversed routes. In this work, commonly traversed routes are identified with clusters based on sensed data. Our framework does not rely on any one source of location information, but can fuse data from multimodal localisation sources. We demonstrate the effectiveness of our algorithm by examining the combination of GPS, wireless signal strength readings and image-matching on very challenging data in a variety of environmental conditions. By fusing these three modalities we obtained better performance than any individual or combination of two modalities. As it can be orientated towards the needs and capabilities of the user based on context, this method becomes useful for some ambient assisted living applications.
Discrete Wavelet Transform and Data Expansion Reduction in Homomorphic Encrypted Domain

Signal processing in the encrypted domain is a new technology with the goal of protecting valuable signals from insecure signal processing. In this paper, we propose a method for implementing discrete wavelet transform (DWT) and multiresolution analysis (MRA) in homomorphic encrypted domain. We first suggest a framework for performing DWT and inverse DWT (IDWT) in the encrypted domain, then conduct an analysis of data expansion and quantization errors under the framework. To solve the problem of data expansion, which may be very important in practical applications, we present a method for reducing data expansion in the case that both DWT and IDWT are performed. With the proposed method, multilevel DWT/IDWT can be performed with less data expansion in homomorphic encrypted domain. We propose a new signal processing procedure, where the multiplicative inverse method is employed as the last step to limit the data expansion. Taking a 2-D Haar wavelet transform as an example, we conduct a few experiments to demonstrate the advantages of our method in secure image processing. We also provide computational complexity analyses and comparisons. To the best of our knowledge, there has been no report on the implementation of DWT and MRA in the encrypted domain.
Geometric Invariant Audio Watermarking Based on an LCM Feature

The development of a geometric invariant audio watermarking scheme without degrading acoustical quality is challenging work. This paper proposes a multi-bit spread-spectrum audio watermarking scheme based on a geometric invariant log coordinate mapping (LCM) feature. The LCM feature is very robust to audio geometric distortions. The watermark is embedded in the LCM feature, but it is actually embedded in the Fourier coefficients which are mapped to the feature via LCM, so the embedding is actually performed in the DFT domain without interpolation, thus eliminating completely the severe distortion resulted from the non-uniform interpolation mapping. The watermarked audio achieves high auditory quality in both objective and subjective quality assessments. A mixed correlation between the LCM feature and a key-generated PN tracking sequence is proposed to align the log-coordinate mapping, thus synchronizing the watermark efficiently with only one FFT and one IFFT. Both the theoretical analysis and experimental results show that the proposed audio watermarking scheme is not only resilient against common signal processing operations, including low-pass filtering, MP3 recompression, echo addition, volume change, normalization, test functions in the Stirmark benchmark, and DA/AD conversion, but also has conquered the challenging audio geometric distortion and achieves the best robustness against simultaneous geometric distortions, such as pitch invariant time-scale modification (TSM) by ±20%, tempo invariant pitch shifting by 20%, resample TSM with scaling factors between 75% and 140%, and random cropping by 95%. This is mainly contributed by the proposed geometric invariant LCM feature. To our best knowledge, audio watermarking based on LCM has not been reported before.
Reversible Data Hiding in JPEG Images

Among various digital image formats used in daily life, the Joint Photographic Experts Group (JPEG) is the most popular. Therefore, reversible data hiding (RDH) in JPEG images is important and useful for many applications such as archive management and image authentication. However, RDH in JPEG images is considerably more difficult than that in uncompressed images because there is less information redundancy in JPEG images than that in uncompressed images, and any modification in the compressed domain may introduce more distortion in the host image. Furthermore, along with the embedding capacity and fidelity (visual quality), which have to be considered for uncompressed images, the storage size of the marked JPEG file should be considered. In this paper, based on the philosophy behind the JPEG encoder and the statistical properties of discrete cosine transform (DCT) coefficients, we present some basic insights into how to select quantized DCT coefficients for RDH. Then, a new histogram shifting-based RDH scheme for JPEG images is proposed, in which the zero coefficients remain unchanged and only coefficients with values 1 and -1 are expanded to carry message bits. Moreover, a block selection strategy based on the number of zero coefficients in each 8 × 8 block is proposed, which can be utilized to adaptively choose DCT coefficients for data hiding. Experimental results demonstrate that by using the proposed method we can easily realize high embedding capacity and good visual quality. The storage size of the host JPEG file can also be well preserved.
New Channel Selection Rule for JPEG Steganography

In this paper, we present a new channel selection rule for joint photographic experts group (JPEG) steganography, which can be utilized to find the discrete cosine transform (DCT) coefficients that may introduce minimal detectable distortion for data hiding. Three factors are considered in our proposed channel selection rule, i.e., the perturbation error (PE), the quantization step (QS), and the magnitude of quantized DCT coefficient to be modified (MQ). Experimental results demonstrate that higher security performance can be obtained in JPEG steganography via our new channel selection rule.
A study on security performance of YASS

YASS (Yet another steganographic scheme) is a newly developed JPEG steganographic method. Through embedding data in the randomized 8 times 8 blocks which do not coincide with the 8 times 8 grid used in JPEG compression, it effectively disables the self-calibration process popularly used in today's JPEG steganalyzers. However, with YASS' complicated embedding procedure, the intra- and interblock dependency among the quantized DCT coefficients belonging to the original image is disturbed after the secret message embedding. Furthermore, because of the randomly selection of an 8 times 8 block within a large block and the necessary utilization of error correction code, the amount of information that YASS can embed is largely reduced. In this paper a study on security performance of YASS is reported. Our experimental results have demonstrated that 1) the steganalyzers which utilizes intra- and/or inter-block correlation of JPEG coefficients can break YASS, 2) with embedding the same amount of information bits, the security of YASS is not stronger than that of MB1 when some today's blind JPEG steganalyzers are used.
Automatic Steganographic Distortion Learning Using a Generative Adversarial Network

Generative adversarial network has shown to effectively generate artificial samples indiscernible from their real counterparts with a united framework of two subnetworks competing against each other. In this letter, we first propose an automatic steganographic distortion learning framework using a generative adversarial network, which is composed of a steganographic generative subnetwork and a steganalytic discriminative subnetwork. Via alternately training these two oppositional subnetworks, our proposed framework can automatically learn embedding change probabilities for every pixel in a given spatial cover image. The learnt embedding change probabilities can then be converted to embedding distortions, which can be adopted in the existing framework of minimal-distortion embedding. Under this framework, the distortion function is directly related to the undetectability against the oppositional evolving steganalyzer. Experimental results show that with adversarial learning, our proposed framework can effectively evolve from nearly naïve random $\pm 1$ embedding at the beginning to much more advanced content-adaptive embedding which tries to embed secret bits in textural regions. The security performance is also steadily improved with increasing training iterations.
Exposing fake bitrate video and its original bitrate

Video bitrate, as one of the important factors that reflect the video quality, can be easily manipulated via some video editing softwares. In some forensic scenarios, for example, video uploaders of video-sharing websites may increase video bitrate for seeking more commercial profits. In this paper, we try to detect those fake high bitrate videos, and then further to estimate their original bitrates. The proposed method is mainly based on the fact that if the video bitrate has been increased with the help of video editing software, its essential video quality will not increase at all. By analyzing the quality of the questionable video and a series of its re-encoded versions with different lower bitrates, we can obtain a feature curve to measure the change of the video quality, and then we propose a compact feature vector (3-D) to expose fake bitrate videos and their original bitrates. The experimental results evaluated on both CIF and QCIF raw sequences have shown the effectiveness of the proposed method.
Scheduling algorithms for parallel real-time systems

The potential speedup of applications has motivated the widespread use of multiprocessors in recent years. Future real-time systems are likely to be built on multiprocessor architectures. Efficient scheduling algorithms for multiprocessors are needed so that tasks will have predictable behaviors. 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 precedence constraints.
Analysis of the role played by error correcting coding in robust watermarking

Robustness is one of the fundamental requirements of digital watermarking for some applications. By modeling digital watermarking as digital communications, many authors proposed to use error correcting coding (ECC) to improve watermarking robustness. However, a fact has long been neglected. That is, due to the requirement of imperceptibility for watermarking, the redundancy introduced by ECC will lead to a decrease of the watermark strength. Hence, a question arises. Could ECC effectively improve the robustness of watermark? This paper investigates this problem from both theoretical analysis and experimental work perspectives. Our conclusion is that ECC cannot effectively improve the robustness of watermarking against most attacks, in particularly, conventional signal processing procedures and noise corruption. The only exception is attacks such as cropping, where ECC possibly improves the robustness of watermarking.
A Study on the Randomness Measure of Image Hashing

How to measure the security of image hashing is still an open issue in the field of image authentication. Some works have been conducted on the security measure of image hashing. One of the most important works is the randomness measure proposed by Swaminathan, which uses differential entropy as a metric to evaluate the security of randomized image features and has been applied mainly in the security analysis of the feature extraction stage of image hashing. It is meaningful to measure the randomness of the image features over the secret-key set for the security of image hashing because the image features extracted by image hashing should be generated randomly and difficult to guess. However, as is well known, differential entropy is not invariant to scaling; thus it might not be enough to evaluate the security of randomized image features. In this paper, we show the fact that if the image features of an image hash function are scaled by a constant that is large than one, then the tradeoff between the robustness and the fragility of the image hash function will not change at all, but the security indicated by the randomness measure will increase. The above-mentioned fact seems to contradict the following. First, the security of image hashing, which conflicts with robustness and fragility, cannot increase freely. Secondly, a deterministic operation, such as deterministic scaling, does not change the security of image hashing in terms of the difficulty of guessing the secret key or randomized image features. Therefore, the randomness measure should be modified to be invariant to scaling at least.
Detection of double compression with the same bit rate in MPEG-2 videos

The detection of double compression with the same bit rate in MPEG-2 is of great significance in the field of video forensics. However, when the singly compressed and the doubly compressed videos have the same bit rate, no detecting method has been reported yet. In this paper, we propose a new method that can detect double MPEG-2 compression with the same bit rate. Our method is based on the observation that the number of different coefficients between I frames of the singly and doubly compressed MPEG-2 videos is much larger than the number of different coefficients between the I frames of the corresponding doubly and triply compressed MPEG-2 videos. Via a new re-compressing strategy, the aforementioned characteristics of MPEG-2 video can be utilized to discriminate the singly compressed and doubly compressed MPEG-2 videos. Various experiments on different bit rates demonstrate the efficiency of our new method.
A new cost function for spatial image steganography

A well defined cost function is crucial to steganography under the scenario of minimizing embedding distortion. In this paper, we present a new cost function for spatial image steganography. The proposed cost function is designed by using a high-pass filter to locate the less predictable parts in an image, and then using two low-pass filters to make the low cost values more clustered. Experiments show that the steganographic method with the proposed cost function makes the embedding changes more concentrated in texture regions, and thus achieves a better performance on resisting the state-of-the-art steganalysis over prior works, including HUGO, WOW, and S-UNIWARD.
An image copy detection scheme based on radon transform

Copy detection aims to identify various content-preserving copies from the same origin and distinguish the different sources. In this paper, we propose a novel feature for image copy detection based on the radon transform (RT). We first theoretically analyze that the proposed feature is invariant against rotation, scaling and translation (RST) operations and robust to lossy JPEG compression and additive noises. The feature can meet the robustness requirements of copy detection applications. The proposed feature is statistically dependent of image content and is capable of capturing unique information of the image for fragility of the copy detection system. The experimental results show that the proposed scheme outperforms the existing methods, when detecting image copies subjected to various transformations, in terms of receiver operating characteristic (ROC) curves.
A convolutive mixing model for shifted double JPEG compression with application to passive image authentication

The artifacts by JPEG recompression have been demonstrated to be useful in passive image authentication. In this paper, we focus on the shifted double JPEG problem, aiming at identifying if a given JPEG image has ever been compressed twice with inconsistent block segmentation. We formulated the shifted double JPEG compression (SD-JPEG) as a noisy convolutive mixing model mostly studied in blind source separation (BSS). In noise free condition, the model can be solved by directly applying the independent component analysis (ICA) method with minor constraint to the contents of natural images. In order to achieve robust identification in noisy condition, the asymmetry of the independent value map (IVM) is exploited to obtain a normalized criteria of the independency. We generate a total of 13 features to fully represent the asymmetric characteristic of the independent value map and then feed to a support vector machine (SVM) classifier. Experiment results on a set of 1000 images, with various parameter settings, demonstrated the effectiveness of our method.
Multi-Scale Analysis Strategies in PRNU-Based Tampering Localization

Accurate unsupervised tampering localization is one of the most challenging problems in digital image forensics. In this paper, we consider a photo response non-uniformity analysis and focus on the detection of small forgeries. For this purpose, we adopt a recently proposed paradigm of multi-scale analysis and discuss various strategies for its implementation. First, we consider a multi-scale fusion approach, which involves combination of multiple candidate tampering probability maps into a single, more reliable decision map. The candidate maps are obtained with sliding windows of various sizes and thus allow to exploit the benefits of both the small- and large-scale analyses. We extend this approach by introducing modulated threshold drift and content-dependent neighborhood interactions, leading to improved localization performance with superior shape representation and easier detection of small forgeries. We also discuss two novel alternative strategies: a segmentation-guided approach, which contracts the decision statistic to a central segment within each analysis window and an adaptive-window approach, which dynamically chooses analysis window size for each location in the image. We perform extensive experimental evaluation on both synthetic and realistic forgeries and discuss in detail practical aspects of parameter selection. Our evaluation shows that the multi-scale analysis leads to significant performance improvement compared with the commonly used single-scale approach. The proposed multi-scale fusion strategy delivers stable results with consistent improvement in various test scenarios.
Steganalysis of LSB Greedy Embedding Algorithm for JPEG Images using Coefficient Symmetry

A recently developed LSB greedy embedding algorithm for JPEG images is capable of resisting the chi-square attack. By carefully studying the quantized DCT (discrete cosine transform) coefficients of the cover and stego images, we find that the embedding algorithm does not preserve the histogram of the DCT coefficients well. In this paper, we define a new chi-square statistic which is used to measure whether the image under scrutiny is like the cover or the stego. Our proposed steganalytic method is based on the symmetry property of the DCT coefficients in JPEG images. It can also be used in the scenario where the cover images are double JPEG compressed. The reliability of this specific steganalytic scheme depends on the embedding rate and it is influenced by the JPEG quality factor. Experimental results show that when the embedding rate exceeds half of the maximal embedding capacity, the steganographic algorithm is detectable with a very low false negative rate, whatever the quality factor is.