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As deep-learning based image and video manipulation technology advances, the future of truth and information looks bleak. In particular, Deepfakes, wherein a person’s face can be transferred onto the face of someone else, pose a serious threat for potential spread of convincing misinformation that is drastic and ubiquitous enough to have catastrophic real-world consequences. To prevent this, an effective detection tool for manipulated media is needed. However, the detector cannot just be good, it has to evolve with the technology to keep pace with or even outpace the enemy. At the same time, it must defend against different attack types to which deep learning systems are vulnerable. To that end, in this paper, we review various methods of both attack and defense on AI systems, as well as modes of evolution for such a system. Then, we put forward a potential system that combines the latest technologies in multiple areas as well as several novel ideas to create a detection algorithm that is robust against many attacks and can learn over time with unprecedented effectiveness and efficiency. 

Risk patterns are crucial in biomedical research and have served as an important factor in precision health and disease prevention. Despite recent development in parallel and high-performance computing, existing risk pattern mining methods still struggle with problems caused by large-scale datasets, such as redundant candidate generation, inability to discover long significant patterns, and prolonged post pattern filtering. In this article, we propose a novel dynamic tree structure, Risk Hierarchical Pattern Tree (RHPTree), and a top-down search method, RHPSearch, which are capable of efficiently analyzing a large volume of data and overcoming the limitations of previous works. The dynamic nature of the RHPTree avoids costly tree reconstruction for the iterative search process and dataset updates. We also introduce two specialized search methods, the extended target search (RHPSearch-TS) and the parallel search approach (RHPSearch-SD), to further speed up the retrieval of certain items of interest. Experiments on both UCI machine learning datasets and sampled datasets of the Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC) datasets demonstrate that our method is not only faster but also more effective in identifying comprehensive long risk patterns than existing works. Moreover, the proposed new tree structure is generic and applicable to other pattern mining problems. 

With the advances in autonomous vehicles and intelligent intersection management systems, traffic lights may be replaced by optimal travel plans calculated for each passing vehicle in the future. While these technological advancements are envisioned to greatly improve travel efficiency, they are still facing various challenging security hurdles since even a single deviation of a vehicle from its assigned travel plan could cause a serious accident if the surrounding vehicles do not take necessary actions in a timely manner. In this paper, we propose a novel security mechanism namely NWADE which can be integrated into existing autonomous intersection management systems to help detect malicious vehicle behavior and generate evacuation plans. In the NWADE mechanism, we introduce the neighborhood watch concept whereby each vehicle around the intersection will serve as a watcher to report or verify the abnormal behavior of any nearby vehicle and the intersection manager. We propose a blockchainbased verification framework to guarantee the integrity and trustworthiness of the individual travel plans optimized for the entire intersection. We have conducted extensive experimental studies on various traffic scenarios, and the experimental results demonstrate the practicality, effectiveness, and efficiency of our mechanism. 

Anonymous communication, that is secure end-to-end and unlinkable, plays a critical role in protecting user privacy by preventing service providers from using message metadata to discover communication links between any two users. Techniques, such as Mix-net, DC-net, time delay, cover traffic, Secure Multiparty Computation (SMC) and Private Information Retrieval, can be used to achieve anonymous communication. SMC-based approach generally offers stronger simulation based security guarantee. In this paper, we propose a simple and novel SMC approach to establishing anonymous communication, easily implementable with two non-colluding servers which have only communication and storage related capabilities. Our approach offers stronger security guarantee against malicious adversaries without incurring a great deal of extra computation. To show its practicality, we implemented our solutions using Chameleon Cloud to simulate the interactions among a million users, and extensive simulations were conducted to show message latency with various group sizes. Our approach is efficient for smaller group sizes and sub-group communication while preserving message integrity. Also, it does not have the message collision problem.