Search

With the advent of the 5G Internet of Things era, communication and social interaction in our daily life have changed a lot, and a large amount of social data is transmitted to the Internet. At the same time, with the rapid development of deep forgery technology, a new generation of social data trust crisis has also followed. Therefore, how to ensure the trust and credibility of social data in the 5G Internet of Things era is an urgent problem to be solved. This paper proposes a new method for forgery detection based on GANs. We first discover the hidden gradient information in the grayscale image of the forged image and use this gradient information to guide the generation of forged traces. In the classifier, we replace the traditional binary loss with the focal loss that can focus on difficult-to-classify samples, which can achieve accurate classification when the real and fake samples are unbalanced. Experimental results show that the proposed method can achieve high accuracy on the DeeperForensics dataset and with the highest accuracy is 98%.

Lots of Deepfake videos are circulating on the Internet, which not only damages the personal rights of the forged individual, but also pollutes the web environment. What’s worse, it may trigger public opinion and endanger national security. Therefore, it is urgent to fight deep forgery. Most of the current forgery detection algorithms are based on convolutional neural networks to learn the feature differences between forged and real frames from big data. In this paper, from the perspective of image generation, we simulate the forgery process based on image generation and explore possible trace of forgery. We propose a multi-scale self-texture attention Generative Network(MSTA-Net) to track the potential texture trace in image generation process and eliminate the interference of deep forgery post-processing. Firstly, a generator with encoder-decoder is to disassemble images and performed trace generation, then we merge the generated trace image and the original map, which is input into the classifier with Resnet as the backbone. Secondly, the self-texture attention mechanism(STA) is proposed as the skip connection between the encoder and the decoder, which significantly enhances the texture characteristics in the image disassembly process and assists the generation of texture trace. Finally, we propose a loss function called Prob-tuple loss restricted by classification probability to amend the generation of forgery trace directly. To verify the performance of the MSTA-Net, we design different experiments to verify the feasibility and advancement of the method. Experimental results show that the proposed method performs well on deep forged databases represented by FaceForensics++, Celeb-DF, Deeperforensics and DFDC, and some results are reaching the state-of-the-art. [ABSTRACT FROM AUTHOR]

The growth and evolution of the World Wide Web (WWW) has been rapid over the last ten years and this has been caused mainly by factors such as the social Web and mobile technology. This growth, which presupposes the satisfaction of millions of users accessing Web applications with an adequate quality of service, requires continuous changes in the infrastructure to improve user experience or to handle new demands. Therefore, studies of Web-based systems aimed at comparing different hardware infrastructures, detecting system bottlenecks, provisioning hardware resources, making capacity planning tests, or software testability, are a matter of huge interest. However, the new trends in the WWW have brought new types of user demands and interactions that produce complex workload patterns. These patterns must be exhaustively studied and considered when designing helpful workload generators able to produce representative traces of the current reality. This survey is aimed at providing a useful guide for researchers of the Web, social networking, and other Internet related issues, regarding the main points and concerns about workload generation for Web-based systems. This paper reviews the predominant characteristics and attributes that define Web workloads, including the special cases of other types of Web applications (e.g., blogs, online social network platforms, and video-sharing services). It also identifies the main challenges for the next generation of Web workload generators, and explores current approaches and solutions suggested in recent works. [ABSTRACT FROM AUTHOR]

This paper presents two methods to extract stops and trips from GPS traces: the first one focuses on periods of non-movement (stops) and the second one tries to identify the longest periods of movement (trips). A stop corresponds to a location where the individual halts with the intention to perform an activity. In order to assert the quality of both methods, the results are compared to cases where the stops and trips are known by other means. First a set of traces was used for which the stops were identified by the traveler by means of a visual tool aimed at alignment of manually reported periods in the diary to automatically recorded GPS coordinates. Second, a set of synthetic traces was used. Several quality indicators are presented; they have been evaluated using sensitivity analysis in order to determine the optimal values for the detector's configuration settings. Person traces (as opposed to car traces) were used. Individual specific behavior seems to have a large effect on the optimal values for threshold settings used in both the TRIP and STOP detector algorithms. Accurate detection of stops and trips in GPS traces is vital to prompted recall surveys because those surveys can extend over several weeks. Inaccurate stop detection requires frequent corrections by the respondent and can cause them to quit. [ABSTRACT FROM AUTHOR]

Cooperative driving is an emerging area in the field of vehicular networks and Intelligent Transportation Systems (ITS). It contributes to enhancing safety by reducing accidents as a consequence of rogue driving and overtaking. Thus, to accomplish the requirements of safety, real-time traffic traces are required for comprehensive analysis. However, the real traces are limited in availability and constraint to the environment they are collected from. On the other hand, a common practice is to use a single-lane traffic generation model to address collision modeling, which is limited by collision modeling of rear-end collisions. Therefore, to address these limitations in the literature, this paper proposes a multilane traffic generation (MLTG) model using microscopic modeling. Furthermore, to generate different multilane synthetic traffic traces, traffic flow behaviors and collision scenarios are replicated as use cases by incorporating the perception & human errors. The efficacy of the MLTG model is tested and analyzed by performing extensive simulations in MATLAB by considering varied scenarios such as straight road, merging, and diversion for single-lane and cross-lane collision modeling, respectively. The results show that the performance of the proposed MLTG model outperforms in terms of stability and scalability, compared to the existing state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In recent years, on-chip trace generation has been recognized as a solution to the debugging of increasingly complex software. An execution trace can be seen as the most fundamentally useful type of trace, allowing the execution path of software to be determined post hoc. However, the bandwidth required to output such a trace can be excessive. Our architecture-aware trace compression (AATC) scheme adds an on-chip branch predictor and branch target buffer to reduce the volume of execution trace data in real time through on-chip compression. Novel redundancy reduction strategies are employed, most notably in exploiting the widespread use of linked branches and the compiler-driven movement of return addresses between link register, stack, and program counter. In doing so, the volume of branch target addresses is reduced by 52%, whereas other algorithmic improvements further decrease trace volume. An analysis of spatial and temporal redundancy in the trace stream allows a comparison of encoding strategies to be made for systematically increasing compression performance. A combination of differential, Fibonacci, VarLen, and Move-to-Front encodings are chosen to produce two compressor variants: a performance-focused xAATC that encodes 56.5 instructions/bit using 24,133 gates and an area-efficient fAATC that encodes 48.1 instructions/bit using only 9,854 gates. [ABSTRACT FROM AUTHOR]

In software debugging, trace generation techniques are used to resolve highly complex bugs. However, the emulators increasingly used for embedded software development do not yet offer the types of trace generation infrastructure available in hardware. In this article, we make changes to the ARM ISA emulation of the QEMU emulator to allow for continuous instruction-level trace generation. Using a standard GDB client, tracepoints can be inserted to dynamically log registers and memory addresses without altering executing code. The ability to run trace experiments in five different modes allows the scope of trace generation to be narrowed as needed, down to the level of a single Linux process. Our scheme collects the execution traces of a Linux process on average between 9.6x-0.7x the speed of existing QEMU trace capabilities, with 96.7% less trace data volume. Compared to a software-instrumented tracing scheme, our method is both unobtrusive and performs on average between 3--4 orders of magnitude faster. [ABSTRACT FROM AUTHOR]

Programmers are writing a rapidly growing number of programs in object-oriented languages, such as Java and C#, that require garbage collection. Garbage collection traces and simulation speed up research by enabling deeper understandings of object lifetime behavior and quick exploration and design of new garbage collection algorithms. When generating perfect traces, the brute-force method of computing object lifetimes requires a whole-heap garbage collection at every potential collection point in the program. Because this process is prohibitively expensive, researchers often use granulated traces by collecting only periodically, for example, every 32 KB of allocation. We extend the state of the art for simulating garbage collection algorithms in two ways. First, we develop a systematic methodology for simulation studies of copying garbage collection and present results showing the effects of trace granularity on these simulations. We show that trace granularity often distorts simulated garbage collection results compared with perfect traces. Second, we present and measure the performance of a new algorithm called Merlin for computing object lifetimes. Merlin timestamps objects and later uses the timestamps of dead objects to reconstruct when they died. The Merlin algorithm piggybacks on garbage collections performed by the base system. Experimental results show that Merlin can generate traces over two orders of magnitude faster than the brute-force method which collects after every object allocation. We also use Merlin to produce visualizations of heap behavior that expose new object lifetime behaviors. [ABSTRACT FROM AUTHOR]