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

Measurement of multi-wall carbon nanotube penetration through a screen filter and single-fiber analysis

In this study, we carried out experiments to study penetration of airborne carbon nanotubes (CNTs) through a screen filter. An electrospray system was employed to aerosolize suspensions of multi-wall CNTs. The generated airborne CNTs were characterized by electron microscopy, and the length and diameter were measured. In the filtration experiments, the challenging CNTs are classified by a differential mobility analyzer. Monodisperse CNTs with the same electrical mobility were then employed to challenge the screen filter. Penetration was measured for CNTs in the range of 100-400nm mobility diameters. The results showed that the CNT penetration was less than the penetration for a sphere with the same mobility diameter, which was mainly due to the larger interception length of the CNTs. We compared the modeling results using single-fiber filtration efficiency theories with the experimental data, and found that the effective interception length can be approximated by the CNT aerodynamic diameter multiplying a scaling factor. A hypothesis is proposed to understand the observatio

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

A Theoretical and Strategic Framework for Information Systems Adoption in Supply Chain Management

Data, information and knowledge are critical assets to the performance of logistics and supply chain management (SCM), because they provide the basis upon which management can plan logistics operations, organize logistics and supply chain (SC) processes, coordinate and communicate with business partners, conduct functional logistics activities, and perform managerial control of physical flow of goods, information exchange and sharing among SC partners. In this paper, we firstly discuss the theories related to IS/IT adoption, and then we discuss a strategic framework and finally, strategies for IS/IT adoption in SCM context are provided

Impact of Social Media Management Styles on Willingness to Be a Fan: A Transaction Cost Economics Perspective

This study investigates the impacts of different styles of social media management on user’s willingness to be a fan. Six companies’ brand-pages on a social media site are examined. Data are collected using survey and interview with a group of social media users. Qualitative data analyses are conducted based on 30 observation reports and 60 open-ended surveys, with follow-up interviews. Grounded on the theoretical lens of transaction cost economics, we find that companies successful in attracting more fans adopt the bilateral governance structure (with frequent updates and mixed asset specificity) in their social media transactions. They are relatively more dedicated and allocate more amounts of resources in their social media interactions. Practicing the right governance structure is demonstrated to be more preferable to the fans, able to attract more engagement and generate organic media in the long run. This is because it is helpful for creating positive perceptions of a brand-page, and fans find it useful in reducing their efforts in information searching and product procurement and social networking costs; and this in turn shows to positively impact one’s willingness to be a fan of the page, which can possibly create the opportunities to be a potential customer, leading to future purchases from the brand. This study also identifies the key concepts or sub-constructs of (user’s) willingness to be a fan of a brand-page in the context of social media. They are brand-page management style (dedicated, caring, responsive), contents (quality, usefulness, diversity) and product (uniqueness, variety, popularity). Available at: https://aisel.aisnet.org/pajais/vol11/iss2/2

Weakly-supervised Part-Attention and Mentored Networks for Vehicle Re-Identification

Vehicle re-identification (Re-ID) aims to retrieve images with the same vehicle ID across different cameras. Current part-level feature learning methods typically detect vehicle parts via uniform division, outside tools, or attention modeling. However, such part features often require expensive additional annotations and cause sub-optimal performance in case of unreliable part mask predictions. In this paper, we propose a weakly-supervised Part-Attention Network (PANet) and Part-Mentored Network (PMNet) for Vehicle Re-ID. Firstly, PANet localizes vehicle parts via part-relevant channel recalibration and cluster-based mask generation without vehicle part supervisory information. Secondly, PMNet leverages teacher-student guided learning to distill vehicle part-specific features from PANet and performs multi-scale global-part feature extraction. During inference, PMNet can adaptively extract discriminative part features without part localization by PANet, preventing unstable part mask predictions. We address this Re-ID issue as a multi-task problem and adopt Homoscedastic Uncertainty to learn the optimal weighing of ID losses. Experiments are conducted on two public benchmarks, showing that our approach outperforms recent methods, which require no extra annotations by an average increase of 3.0% in CMC@5 on VehicleID and over 1.4% in mAP on VeRi776. Moreover, our method can extend to the occluded vehicle Re-ID task and exhibits good generalization ability.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

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