nils-herrmann/clean_scirepeval huggingface dataset:
CDMA-based watermarking resisting to cropping

In this paper, we propose an image watermarking algorithm in DWT domain with self-synchronization, focusing on resisting to cropping attack. In embedding, the CDMA encoded watermark, concatenated by the synchronization code, is embedded into the selected DWT coefficients of the original image. During the oblivious watermarking detection, the corresponding DWT coefficients of the cropped watermarked image are relocated by self-synchronizing efficiently, and then the correlation between the extracted watermark information and the Gold sequence is analyzed. By exploiting the time-frequency localization characteristics of DWT, we reduce the computation load to search the synchronization code. The experiment results demonstrate that the thus generated watermark is robust to cropping, JPEG, Gaussian noise and other common attacks.
Compression history identification for digital audio signal

Compression history identification plays a very important role in digital multimedia forensics. However, most existing literatures mainly focus on digital image forensics, and just a few works consider digital audio. In this paper, we investigate two popular compression schemes in digital audio, that is, MP3 and WMA, and try to reveal the compression history for a questionable audio signal in the original uncompressed WAV format via analyzing some statistical characteristics of the modified discrete cosine transform coefficients of the audio. The extensive experimental results have shown that the proposed method can effectively identify whether the given audio has been previously compressed with MP3 and/or WMA, and can further estimate the hidden compression rates, even the compression rate is as high as 128 K bps (bits per second).
Improving Robustness of Quantization-Based Image Watermarking via Adaptive Receiver

In this paper, the watermarking channel is modeled as a generalized channel with fading and nonzero mean additive noise. In order to improve the watermark robustness against the generalized channel, we present an optimized watermark extraction scheme by using an adaptive receiver for quantization-based watermarking. In the proposed extraction scheme, we adaptively estimate the decision zone of the binary data bits and the quantization step size. A training sequence is embedded into the original image together with the informative watermark. The estimation of the decision zone takes advantage of the response function of the training sequence. Compared to those watermarking schemes without receiver adaptation, the main improvement is the enhanced robustness against median filtering, image intensity Direct Current (DC) change, histogram equalization, color reduction, image intensity linear scaling, image intensity nonlinear scaling such as Gamma correction etc.
Steganalysis of Multiple-Base Notational System Steganography

This letter presents a method for attacking multiple-base notational system (MBNS) steganography . In the MBNS steganography, secret data are converted into symbols in a notational system with multiple bases. The pixels of a host image are then modified such that when the pixel values are divided by the bases, their remainders are equal to the symbols. Through analysis, we prove that the amount of small remainders increases due to the modification. Based on this observation, we propose a steganalytic approach which is effective in not only detecting MBNS steganography but also estimating its embedding rate.
Fragility Analysis of Adaptive Quantization-Based Image Hashing

Fragility is one of the most important properties of authentication-oriented image hashing. However, to date, there has been little theoretical analysis on the fragility of image hashing. In this paper, we propose a measure called expected discriminability for the fragility of image hashing and study this fragility theoretically based on the proposed measure. According to our analysis, when Gray code is applied into the discrete-binary conversion stage of image hashing, the value of the expected discriminability, which is dominated by the quantization stage of image hashing, is no more than 1/2. We further evaluate the expected discriminability of the image-hashing scheme that uses adaptive quantization, which is the most popular quantization scheme in the field of image hashing. Our evaluation reveals that if deterministic adaptive quantization is applied, then the expected discriminability of the image-hashing scheme can reach the maximum value (i.e., 1/2). Finally, some experiments are conducted to validate our theoretical analysis and to compare the performance of several quantization schemes for image hashing.
Controllable Secure Watermarking Technique for Tradeoff Between Robustness and Security

The circular watermarking (CW) technique has attracted increasing attention because it can resist the estimation of secret carriers in the watermarked only attack (WOA) framework. However, the existing CW schemes are not applicable whenever a malicious watermark removal attack can take place. This is because they either have low security because the attacker can disclose the embedding subspace or have low robustness. Based on an existing CW scheme called transportation natural watermarking (TNW), this correspondence presents a new CW technique for the tradeoff between robustness and security, which we refer to as controllable secure watermarking (CSW). The idea behind the CSW is that by altering the host signal in the orthogonal complement of the embedding subspace, we can make the watermarked signal have an orthogonally invariant distribution in a higher dimensional subspace including the embedding subspace. Orthogonally invariant distribution essentially requires that the distribution does not change if multiplied by any freely chosen orthogonal matrix, and the higher dimensional subspace is referred to as invariant subspace. We prove that the attacker can only reduce the uncertainty of secret carriers up to the invariant subspace. The dimension of the invariant subspace can be used for the tradeoff between robustness and security. Further, the experiment results show that the robustness-security tradeoff provided by the CSW is efficient. In particular, with the increase of the dimension of the invariant subspace, the security of the CSW will increase quickly while its robustness will only decrease slowly.
Provably secure and ID-based group signature scheme

There are two important directions in group signature scheme: ID-based group signature scheme and provably secure group signature scheme. We first analyze the advantages and flaws of these two directions, and propose a provably secure ID-based group signature scheme basing on the ACJT group signature scheme. Compared with prior ID-Based group signature scheme, our scheme is provably secure in random oracle model. Compared with the ACJT scheme, our scheme is ID-Based and strong nonrepudiation.
Power constrained multiple signaling in digital image watermarking

A watermark signal, which is a unique sequence of random variables, by itself does not give a decisive indication of ownership. This work addresses the need to include meaningful information such that a string of English characters, numbers, and punctuation is embedded within the watermark signal, while the signal remains a sequence of random variables. We attempt to provide answers for (1) the maximum number of symbols the watermark signal can carry, and (2) the most practical and reliable way to implement this. We approached this problem as power constrained multiple signaling over an AWGN channel.
Blind Detection of Median Filtering in Digital Images: A Difference Domain Based Approach

Recently, the median filtering (MF) detector as a forensic tool for the recovery of images' processing history has attracted wide interest. This paper presents a novel method for the blind detection of MF in digital images. Following some strongly indicative analyses in the difference domain of images, we introduce two new feature sets that allow us to distinguish a median-filtered image from an untouched image or average-filtered one. The effectiveness of the proposed features is verified with evidence from exhaustive experiments on a large composite image database. Compared with prior arts, the proposed method achieves significant performance improvement in the case of low resolution and strong JPEG post-compression. In addition, it is demonstrated that our method is more robust against additive noise than other existing MF detectors. With analyses and extensive experimental researches presented in this paper, we hope that the proposed method will add a new tool to the arsenal of forensic analysts.
Multi-resolution access control algorithm based on fractal coding

This paper presents, a new technique for compression and secure image coding that is based on the iterated function system (IFS). A multi-resolution access control algorithm based on the convergence control of an iterative image reconstruction process is investigated. For multimedia applications an icon representation of low image quality is extracted directly from the encrypted data flow thanks to the partial self-affinity properties inherent to the local-IFS. The robustness of our scheme is due to the inherent high correlation of image data and to the non-linear decoding process.
Robust foreground segmentation using improved Gaussian Mixture Model and optical flow

In automatic video surveillance applications, one of the most popular topics consists of separating the moving objects from the static part of the scene. In this context, Gaussian Mixture Model (GMM) background subtraction has been widely employed. It is based on a probabilistic approach that achieves satisfactory performance thanks to its ability to handle complex background scenes. However, the background model estimation step is still problematic; the main difficulty is to decide which distributions of the mixture belong to the background. To achieve an improved overall performance, motion cue could provide a rich source of information about the scene. Therefore, in this paper, we propose a new approach based on incorporating an uniform motion model into GMM background subtraction. By considering these both cues, high accuracy of foreground segmentation is obtained. Our approach has been experimentally validated showing better segmentation performance by comparisons with other approaches published in the literature.
Improving identification by pruning: A case study on face recognition and body soft biometric

We investigate body soft biometrics capabilities to perform pruning of a hard biometrics database improving both retrieval speed and accuracy. Our pre-classification step based on anthropometric measures is elaborated on a large scale medical dataset to guarantee statistical meaning of the results, and tested in conjunction with a face recognition algorithm. Our assumptions are verified by testing our system on a chimera dataset. We clearly identify the trade off among pruning, accuracy, and mensuration error of an anthropomeasure based system. Even in the worst case of ±10% biased anthropometric measures, our approach improves the recognition accuracy guaranteeing that only half database has to be considered.
Physiological and behavioural approaches for person recognition using videos

In this article we present two physiological and behavioural approaches for person recognition using videos. The first system, called the multimodal recognition system, is divided in two modules. The first module exploits the behavioural information: it is based on statistical features computed using the displacement signals of the head; the second one is dealing with the physiological information: it is a probabilistic extension of the classic Eigenface approach. For a consistent fusion, both systems share the same probabilistic classification framework: a Gaussian Mixture Model (GMM) approximation and a Bayesian classifier. The second system, called the tomoface recognition system, applies discrete video tomography to compute spatiotemporal features that summarise the head and facial dynamics of a sequence into a single image (called "video X-ray image"); these novel features are subsequently analysed by an extended version of the eigenface approach. Finally, we assess the performances of both systems, and we compare them with a traditional recognition approach based on facial appearance.
Weight estimation from visual body appearance

Weight is a biometric trait which has been already studied in both the forensic and medical domains. In many practical situations, such as videosurveillance, weight can provide useful information for re-identification purposes but needs to be estimated from visual appearance (images or video). In this work we study the feasibility of weight estimation from anthropométrie data directly accessible from the available image material. A model is retrieved via multiple regression analysis on a set of anthropométrie features. A large medical database is exploited for the model training, while its validation is performed both on ideal and realistic conditions. The performance analysis of our approach shows that under noisy data conditions the system provides accurate estimations, putting the basis for a future work towards an automatic weight estimation.
Tomofaces: Eigenfaces extended to videos of speakers

In this article we propose a novel spatio-temporal approach for person recognition using video information. By applying discrete video tomography, our algorithm summarises the head and facial dynamics of a sequence into a single image (called "video X-ray image"), which is subsequently analysed by an extended version of the eigenface approach. In the experimental part, we assess the discriminative power of our system and we compare it with an analogous one working on traditional facial appearance. Finally, we integrate the X-ray information with appearance in a multimodal system, which improves the recognition rates of standalone frameworks.
Boosting cross-age face verification via generative age normalization

Despite the tremendous progress in face verification performance as a result of Deep Learning, the sensitivity to human age variations remains an Achilles' heel of the majority of the contemporary face verification software. A promising solution to this problem consists in synthetic aging/rejuvenation of the input face images to some predefined age categories prior to face verification. We recently proposed [3] Age-cGAN aging/rejuvenation method based on generative adversarial neural networks allowing to synthesize more plausible and realistic faces than alternative non-generative methods. However, in this work, we show that Age-cGAN cannot be directly used for improving face verification due to its slightly imperfect preservation of the original identities in aged/rejuvenated faces. We therefore propose Local Manifold Adaptation (LMA) approach which resolves the stated issue of Age-cGAN resulting in the novel Age-cGAN+LMA aging/rejuvenation method. Based on Age-cGAN+LMA, we design an age normalization algorithm which boosts the accuracy of an off-the-shelf face verification software in the cross-age evaluation scenario.
On the vulnerability of face recognition systems to spoofing mask attacks

There are several types of spoofing attacks to face recognition systems such as photograph, video or mask attacks. To the best of our knowledge, the impact of mask spoofing on face recognition has not been analyzed yet. The reason for this delay is mainly due to the unavailability of public mask attacks databases. In this study, we use a 2D+3D mask database which was prepared for a research project in which the authors are all involved. This paper provides new results by demonstrating the impact of mask attacks on 2D, 2.5D and 3D face recognition systems. The results show that face recognition systems are vulnerable to mask attacks, thus countermeasures have to be developed to reduce the impact of mask attacks on face recognition. The results also show that 2D texture analysis provides more information than 3D face shape analysis in order to develop a countermeasure against high-quality mask attacks.
Person Recognition based on Head and Mouth Dynamics

Face is considered as an attractive biometric but because of multiple sources of variabilities, the associated recognition rate is not high enough, when working on appearance only, for most of real applications. Considering that most available visual data are videos and not still images, we investigate in this article the possible contribution of some dynamic parameters (head displacements and mouth motion) in person recognition. Some preliminary results tend to validate this original proposal that opens some new perspectives in the possible design of future hybrid and efficient system combining appearance and dynamics of faces
Object detection with a minimal set of examples using Convolutional PCA

Current object detection systems reach high detection rates, at the expense of requiring a large training database. This paper presents a new method for object detection, that gives state-of-the-art results, while using a reduced training database. The proposed system relies on a new local feature extraction approach inspired by Convolutional Neural Networks, Principal Component Analysis and Multilayer Perceptrons. We show that the proposed scheme improves robustness and generalization on the specific problem of face detection, with a very reduced set of exemplar face images.
From Kinect video to realistic and animatable MPEG-4 face model: A complete framework

The recent success of the Kinect sensor has a significant impact on 3D data based computer applications. This study aims to obtain MPEG-4 compliant realistic and animatable face models from Kinect video. The complete framework for this process includes initially the computation of high quality 3D scans from RGB-D Kinect video, and then the computation of animatable MPEG-4 face models using these high quality scans. This study shows that it is possible to obtain high quality 3D scans and realistic and animatable face models of subjects using lower quality Kinect data.
Facial makeup detection technique based on texture and shape analysis

Recent studies show that the performances of face recognition systems degrade in presence of makeup on face. In this paper, a facial makeup detector is proposed to further reduce the impact of makeup in face recognition. The performance of the proposed technique is tested using three publicly available facial makeup databases. The proposed technique extracts a feature vector that captures the shape and texture characteristics of the input face. After feature extraction, two types of classifiers (i.e. SVM and Alligator) are applied for comparison purposes. In this study, we observed that both classifiers provide significant makeup detection accuracy. There are only few studies regarding facial makeup detection in the state-of-the art. The proposed technique is novel and outperforms the state-of-the art significantly.
Contextualized Privacy Filters in Video Surveillance Using Crowd Density Maps

The widespread growth in the adoption of digital video surveillance systems emphasizes the need for privacy preservation video analytics techniques. While these privacy aspects have shown big interest in recent years, little importance has been given to the concept of context-aware privacy protection filters. In this paper, we specifically focus on the dependency between privacy preservation and crowd density. We show that additional information about the crowd density in the scene can be used in order to adjust the level of privacy protection according to the local needs. This additional information cue consists of modeling time-varying dynamics of the crowd density using local features as an observation of a probabilistic crowd function. It also involves a feature tracking step which enables excluding feature points on the background. This process is favourable for the later density function estimation since the influence of features irrelevant to the underlying crowd density is removed. Then, the protection level of personal privacy in videos is adapted according to the crowd density. Afterwards, a framework for objective evaluation of the contextualized protection filters is proposed. The effectiveness of the proposed context-aware privacy filters has been demonstrated by assessing the intelligibility vs. privacy trade-off using videos from different crowd datasets.
ASePPI, an adaptive scrambling enabling privacy protection and intelligibility in H.264/AVC

The usage of video surveillance systems increases more and more every year and protecting people privacy becomes a serious concern. In this paper, we present ASePPI, an Adaptive Scrambling enabling Privacy Protection and Intelligibility. It operates in the DCT domain within the H.264 standard. For each residual block of the luminance channel inside the region of interest, we encrypt the coefficients. Whereas encrypted coefficients appear as noise in the protected image, the DC value is dedicated to restore some of the original information. Thus, the proposed approach automatically adapts the level of protection according to the resolution of the region of interest. Comparing to existing methods, our framework provides better privacy protection with some flexibilities on the appearance of the protected version yielding better visibility of the scene for monitoring. Moreover, the impact on the source coding stream is negligible. Indeed, the results demonstrate a slight decrease in the quality of the reconstructed images and a small percentage of bits overhead.
Fusing iris colour and texture information for fast iris recognition on mobile devices

A novel approach for fast iris recognition on mobile devices is presented in this paper. Its key features are: (i) the use of a combination of classifiers exploiting the iris colour and texture information; (ii) its limited computational time, particularly suitable for fast identity checking on mobile devices; (iii) the high parallelism of the code, making this approach also appropriate for identity verification on large database. The proposed method has been submitted to the Mobile Iris CHallenge Evaluation II. The test set employed for the contest evaluation is made available on the contest web page. The latter has been used to assess the performance of the proposed method in terms of Recognition Rate (RR) and Area Under Receiver Operating Characteristic Curve (AUC).
Person Recognition Form Video using Facial Mimics

Video based facial recognition is an appealing modality in biometrics due to its acceptability and ease of use but the associated recognition rate is not high enough due to multiple sources of variation and lack of constraints in real world applications. In this article we investigate the possible contribution of facial mimics extracted from low quality videos for person recognition. The initial results reported tend to validate this original proposal, thus opening some new perspectives for design of future hybrid and efficient system combining facial appearance and dynamics.
On discriminative properties of TPS warping parameters for 3D face recognition

Due to the advances in the acquisition systems, three-dimensional (3D) facial shape information has been increasingly used for human face recognition. In order to be able to compare different facial surfaces, various registration approaches have been proposed, including Thin Plate Spline (TPS) based algorithms. In this paper, instead of adopting the TPS for registration purposes, we analyze the discriminative properties of the parameters obtained by deforming a generic face model onto target faces using TPS. The warping parameters (WP) that describe the non-global and non-linear transformations and represent the deviations from the common geometric structure are given to the classifier for face recognition. The descriptiveness of those vectors is analyzed on the FRGC database where total of 4569 3D face models are utilized. In spite of its low complexity compared to other proposed approaches, this method yields promising accuracy rates.