Shan Jia

Bio: Shan Jia is an academic researcher from Wuhan University. The author has contributed to research in topics: Computer science & Spoofing attack. The author has an hindex of 8, co-authored 23 publications receiving 198 citations.

Coarse-to-Fine Copy-Move Forgery Detection for Video Forensics

Abstract: Video copy-move forgery detection is one of the hot topics in multimedia forensics to protect digital videos from malicious use. Several approaches have been presented through analyzing the side effect caused by copy–move operation. However, based on multiple similarity calculations or unstable image features, a few can well balance the detection efficiency, robustness, and applicability. In this paper, we propose a novel approach to detect frame copy–move forgeries in consideration of the three requirements. A coarse-to-fine detection strategy based on optical flow (OF) and stable parameters is designed. Specifically, coarse detection analyzes OF sum consistency to find suspected tampered points. Fine detection is then conducted for precise location of forgery, including duplicated frame pairs matching based on OF correlation and validation checks to further reduce the false detections. Experimental evaluation on three public video data sets shows that the proposed approach is effective and efficient in detecting both unsmooth manipulation and common smooth forgery and also with high robustness to regular attacks, including additive noise, filtering, and compression.

Motion-Adaptive Frame Deletion Detection for Digital Video Forensics

Abstract: The detection of frame deletion forgery is of great significance in the field of video forensics. Existing approaches, however, are not applicable to video sequences with variable motion strengths. In addition, the impact of interfering frames has not been considered in these approaches. Our research aims to develop a motion-adaptive forensic method as well as to eliminate interfering frames. Through a study of the statistical characteristics of the most common interfering frames such as relocated I-frames, we develop a new fluctuation feature based on frame motion residuals to identify frame deletion points (FDPs). The fluctuation feature is further enhanced by an intra-prediction elimination procedure so that it can be adapted to sequences with various motion levels. The enhanced feature is measured using a moving window detector to identify the location of a FDP. Finally, a postprocessing procedure is proposed to eliminate the minor interferences of sudden lighting change, focus vibration, and frame jitter. Our experimental results demonstrate that for videos with variable motion strengths and different interfering frames, the true positive rate of the algorithm can reach 90% when the false alarm rate is 0.3%. Our proposed method could provide a foundation for many practical applications of video forensics.

3D Face Anti-Spoofing With Factorized Bilinear Coding

Abstract: We have witnessed rapid advances in both face presentation attack models and presentation attack detection (PAD) in recent years. When compared with widely studied 2D face presentation attacks, 3D face spoofing attacks are more challenging because face recognition systems are more easily confused by the 3D characteristics of materials similar to real faces. In this work, we tackle the problem of detecting these realistic 3D face presentation attacks and propose a novel anti-spoofing method from the perspective of fine-grained classification. Our method, based on factorized bilinear coding of multiple color channels (namely MC_FBC), targets at learning subtle fine-grained differences between real and fake images. By extracting discriminative and fusing complementary information from RGB and YCbCr spaces, we have developed a principled solution to 3D face spoofing detection. A large-scale wax figure face database (WFFD) with both images and videos has also been collected as super realistic attacks to facilitate the study of 3D face presentation attack detection. Extensive experimental results show that our proposed method achieves the state-of-the-art performance on both our own WFFD and other face spoofing databases under various intra-database and inter-database testing scenarios.

DCCP: an effective data placement strategy for data-intensive computations in distributed cloud computing systems

Abstract: Cloud computing systems provide high-performance computing resources and distributed storage space to deal with data-intensive computations Data scheduling between data centers is becoming indispensable for the cloud computing systems in which a mass of large datasets is stored at different data centers and inter-center data accesses are needed in data analytics However, the performance of data scheduling is highly dependent upon the rationality of data placement Data placement is a key optimization method for reducing data scheduling between data centers and realizing statistical I/O load balancing, accordingly reducing the mean computation execution time This paper proposes a data placement strategy, DCCP, which is developed based on dynamic computation correlation DCCP places the datasets with high dynamic computation correlations at the same data center considering the I/O load and the capacity load of data centers; when computations are scheduled for this data center, most of the datasets they process are stored locally, and thus the mean computation execution time can be reduced Evidence from a large number of experiments proves that the DCCP can achieve the statistical I/O load balancing and the capacity load balancing of data centers, thus reducing the total data scheduling between data centers as much as possible at a very low time complexity, even as the numbers of datasets and data centers increase

Recurrent Convolutional Strategies for Face Manipulation Detection in Videos.

Abstract: The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods. Despite the predominant effort of detecting face manipulation in still images, less attention has been paid to the identification of tampered faces in videos by taking advantage of the temporal information present in the stream. Recurrent convolutional models are a class of deep learning models which have proven effective at exploiting the temporal information from image streams across domains. We thereby distill the best strategy for combining variations in these models along with domain specific face preprocessing techniques through extensive experimentation to obtain state-of-the-art performance on publicly available video-based facial manipulation benchmarks. Specifically, we attempt to detect Deepfake, Face2Face and FaceSwap tampered faces in video streams. Evaluation is performed on the recently introduced FaceForensics++ dataset, improving the previous state-of-the-art by up to 4.55% in accuracy.

A Time-Driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing

Abstract: Compared to traditional distributed computing environments such as grids, cloud computing provides a more cost-effective way to deploy scientific workflows. Each task of a scientific workflow requires several large datasets that are located in different datacenters, resulting in serious data transmission delays. Edge computing reduces the data transmission delays and supports the fixed storing manner for scientific workflow private datasets, but there is a bottleneck in its storage capacity. It is a challenge to combine the advantages of both edge computing and cloud computing to rationalize the data placement of scientific workflow, and optimize the data transmission time across different datacenters. In this study, a self-adaptive discrete particle swarm optimization algorithm with genetic algorithm operators (GA-DPSO) was proposed to optimize the data transmission time when placing data for a scientific workflow. This approach considered the characteristics of data placement combining edge computing and cloud computing. In addition, it considered the factors impacting transmission delay, such as the bandwidth between datacenters, the number of edge datacenters, and the storage capacity of edge datacenters. The crossover and mutation operators of the genetic algorithm were adopted to avoid the premature convergence of traditional particle swarm optimization algorithm, which enhanced the diversity of population evolution and effectively reduced the data transmission time. The experimental results show that the data placement strategy based on GA-DPSO can effectively reduce the data transmission time during workflow execution combining edge computing and cloud computing.

Privacy Preservation in Big Data From the Communication Perspective—A Survey

Abstract: The advancement of data communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the communication perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in communication field.

Face Morphing Attack Generation & Detection: A Comprehensive Survey

Abstract: The vulnerability of Face Recognition System (FRS) to various kind of attacks (both direct and in-direct attacks) and face morphing attacks has received a great interest from the biometric community. The goal of a morphing attack is to subvert the FRS at Automatic Border Control (ABC) gates by presenting the Electronic Machine Readable Travel Document (eMRTD) or e-passport that is obtained based on the morphed face image. Since the application process for the e-passport in the majority countries requires a passport photo to be presented by the applicant, a malicious actor and the accomplice can generate the morphed face image and to obtain the e-passport. An e-passport with a morphed face images can be used by both the malicious actor and the accomplice to cross the border as the morphed face image can be verified against both of them. This can result in a significant threat as a malicious actor can cross the border without revealing the track of his/her criminal background while the details of accomplice are recorded in the log of the access control system. This survey aims to present a systematic overview of the progress made in the area of face morphing in terms of both morph generation and morph detection. In this paper, we describe and illustrate various aspects of face morphing attacks, including different techniques for generating morphed face images but also the state-of-the-art regarding Morph Attack Detection (MAD) algorithms based on a stringent taxonomy and finally the availability of public databases, which allow to benchmark new MAD algorithms in a reproducible manner. The outcomes of competitions/benchmarking, vulnerability assessments and performance evaluation metrics are also provided in a comprehensive manner. Furthermore, we discuss the open challenges and potential future works that need to be addressed in this evolving field of biometrics.

Coarse-to-Fine Copy-Move Forgery Detection for Video Forensics

Abstract: Video copy-move forgery detection is one of the hot topics in multimedia forensics to protect digital videos from malicious use. Several approaches have been presented through analyzing the side effect caused by copy–move operation. However, based on multiple similarity calculations or unstable image features, a few can well balance the detection efficiency, robustness, and applicability. In this paper, we propose a novel approach to detect frame copy–move forgeries in consideration of the three requirements. A coarse-to-fine detection strategy based on optical flow (OF) and stable parameters is designed. Specifically, coarse detection analyzes OF sum consistency to find suspected tampered points. Fine detection is then conducted for precise location of forgery, including duplicated frame pairs matching based on OF correlation and validation checks to further reduce the false detections. Experimental evaluation on three public video data sets shows that the proposed approach is effective and efficient in detecting both unsmooth manipulation and common smooth forgery and also with high robustness to regular attacks, including additive noise, filtering, and compression.