A Lightweight Reliably Quantified Deepfake Detection Approach

Deepfake has brought huge threats to society such that everyone can become a potential victim. Current Deepfake detection approaches have unsatisfactory performance in either accuracy or efficiency. Meanwhile, most models are only evaluated on different benchmark test datasets with different accuracies, which could not imitate the real-life Deepfake unknown population. As Deepfake cases have already been raised and brought challenges at the court, it is disappointed that no existing work has studied the model reliability and attempted to make the detection model act as the evidence at the court. We propose a lightweight Deepfake detection deep learning approach using the convolutional neural network backbone and the efficient convolutional attention mechanism, outperforming the state-of-the-art baseline models on each benchmark test dataset. Furthermore, a real-life Deepfake content is usually unknown about the corresponding source dataset or manipulation technique. We conduct a model reliability study using statistical random sampling from the available benchmark datasets to imitate the real-life Deepfake cases. A sufficient number of trials for model evaluation with random sampling derives the 95% and 90% confidence intervals, informing the reliable accuracy information of the proposed model. As a result, the reliably quantified detection model derives satisfactory accuracy and error rate to be applicable at the court for civil cases and provides an informative scheme to analyze future satisfactory approaches for criminal cases at the court

PLC Forensics Based on Control Program Logic Change Detection

Supervisory Control and Data Acquisition (SCADA) system is an industrial control automated system. It is built with multiple Programmable Logic Controllers (PLCs). PLC is a special form of microprocessor-based controller with proprietary operating system. Due to the unique architecture of PLC, traditional digital forensic tools are difficult to be applied. In this paper, we propose a program called Control Program Logic Change Detector (CPLCD), it works with a set of Detection Rules (DRs) to detect and record undesired incidents on interfering normal operations of PLC. In order to prove the feasibility of our solution, we set up two experiments for detecting two common PLC attacks. Moreover, we illustrate how CPLCD and network analyzer Wireshark could work together for performing digital forensic investigation on PLC

Formal specification and implementation of a Chinese dictionary

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A structured knowledge representation scheme for natural language processing

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Deep Convolutional Pooling Transformer for Deepfake Detection

Recently, Deepfake has drawn considerable public attention due to security and privacy concerns in social media digital forensics. As the wildly spreading Deepfake videos on the Internet become more realistic, traditional detection techniques have failed in distinguishing between real and fake. Most existing deep learning methods mainly focus on local features and relations within the face image using convolutional neural networks as a backbone. However, local features and relations are insufficient for model training to learn enough general information for Deepfake detection. Therefore, the existing Deepfake detection methods have reached a bottleneck to further improve the detection performance. To address this issue, we propose a deep convolutional Transformer to incorporate the decisive image features both locally and globally. Specifically, we apply convolutional pooling and re-attention to enrich the extracted features and enhance efficacy. Moreover, we employ the barely discussed image keyframes in model training for performance improvement and visualize the feature quantity gap between the key and normal image frames caused by video compression. We finally illustrate the transferability with extensive experiments on several Deepfake benchmark datasets. The proposed solution consistently outperforms several state-of-the-art baselines on both within- and cross-dataset experiments.Comment: Accepted to be published in ACM TOM

Deepfake Detection: A Comprehensive Study from the Reliability Perspective

The mushroomed Deepfake synthetic materials circulated on the internet have raised serious social impact to politicians, celebrities, and every human being on earth. In this paper, we provide a thorough review of the existing models following the development history of the Deepfake detection studies and define the research challenges of Deepfake detection in three aspects, namely, transferability, interpretability, and reliability. While the transferability and interpretability challenges have both been frequently discussed and attempted to solve with quantitative evaluations, the reliability issue has been barely considered, leading to the lack of reliable evidence in real-life usages and even for prosecutions on Deepfake related cases in court. We therefore conduct a model reliability study scheme using statistical random sampling knowledge and the publicly available benchmark datasets to qualitatively validate the detection performance of the existing models on arbitrary Deepfake candidate suspects. A barely remarked systematic data pre-processing procedure is demonstrated along with the fair training and testing experiments on the existing detection models. Case studies are further executed to justify the real-life Deepfake cases including different groups of victims with the help of reliably qualified detection models. The model reliability study provides a workflow for the detection models to act as or assist evidence for Deepfake forensic investigation in court once approved by authentication experts or institutions.Comment: 20 pages for peer revie

Estás comigo ou contra mim? : o papel moderador da relação treinador-atleta na relação entre grit e engagement desportivo nos atletas

Dissertação de Mestrado apresentada no ISPA – Instituto Universitário para obtenção de grau de Mestre na especialidade de Psicologia Social e das OrganizaçõesO presente estudo teve como principal objetivo contribuir para o estudo da relação entre treinador e atleta e a testar o seu papel moderador entre na relação entre as variáveis individuais de grit e engagement desportivo dos atletas. Participaram neste estudo 315 atletas de várias modalidades individuais e coletivas. Foi elaborado um questionário aplicado online constituído pela Escala de Relação Treinador-Atleta (CART-Q), a Escala de Grit (GRIT-S) e a Escala de Engagement Desportivo (AEQ). Os resultados demostram que a relação treinador-atleta influencia positivamente o nível de grit e o nível de engagement dos atletas, e evidenciam ainda uma associação positiva entre o nível de grit e o nível de engagement. Verificou-se também que a relação treinador-atleta tem um efeito moderador nos níveis de grit e uma dimensão específica engagement do atleta, a Dedicação. Os resultados são discutidos, analisados e são apresentadas as limitações do estudo, assim como sugestões para estudos futuros.The main goal of this work was to contribute to the study of the relationship between the coach and his athletes and to test its moderating role in the relationship between the individual traits of grit and sport engagement of athletes. A total of 315 Portuguese athletes of both sexes, and diverse collective and individual sports participated in this study. These participants answered an online questionnaire constituted by the Coach-Athlete Relationship (CART-Q), the Grit Scale (GRIT-S) and the Engagement Scale (AEQ). The results showed that the coach-athlete relationship influences positively the levels of grit and sport engagement of the athletes, and also evidence a positive association between the level of grit and the level of engagement. Results also showed that the coach-athlete relationship has a moderator effect on the levels of grit and a specific dimension of engagement, the Dedication. The results are discussed, analyzed and are presented the limitations of the study, as well some suggestions for future studies

The efficacy and safety of Yupingfeng Powder with variation in the treatment of allergic rhinitis: Study protocol for a randomized, double-blind, placebo-controlled trial

Background: Allergic rhinitis (AR) is an upper airways chronic inflammatory disease mediated by IgE, which affects 10%–20% of the population. The mainstay for allergic rhinitis nowadays include steroids and antihistamines, but their effects are less than ideal. Many patients therefore seek Chinese medicine for treatment and Yupingfeng Powder is one of the most common formulae prescribed. In this study, we aim to investigate the efficacy and safety of Yupingfeng Powder with variation for the treatment of allergic rhinitis.Study design: This is a double-blind, randomized, placebo-controlled trial. A 2-week screening period will be implemented, and then eligible subjects with allergic rhinitis will receive interventions of either “Yupingfeng Powder with variation” granules or placebo granules for 8 weeks, followed by post treatment visits at weeks 12 and 16. The change in the Total Nasal Symptom Score (TNSS) will be used as the primary outcome.Discussion: This trail will evaluate the efficacy and safety of Yupingfeng Powder in treating allergic rhinitis. The study may provide the solid evidence of Yupingfeng Powder with variation can produce better clinical efficacy than the placebo granules.Trial registration:ClinicalTrials.gov, identifier NCT04976023