Nowadays,the capability of traditional digital forensic tools fails to meet the demand of ever increasing of criminal or civil cases.One of the challenges is that digital devices and applications are multifarious and changing quickly.Here,we propose a new mode for digital forensic tools utilization via integrating open-source single tools into a platform and setting up into Live DVD/USB.The platform,an Integrated Open Forensic Environment（named IOFE）,takes full advantage of these tools and,at the same time,elevates its power and interoperability via standardized input/output data.The IOFE features conducting live and dead investigation and covers three consecutive major phases of digital forensics：acquisition,analysis,and presentation.Our experiments prove that IOFE can carry out manifold acquisition,interpretation,analysis,and presentation task of evidentiary data in an efficient and effective manner.
In this paper, a novel concept of multilayer synthesis and a general framework for texture synthesis method are presented. Within this framework, we first decompose the texture into the supposed pattern layer and material layer in the frequency domain by an E-texton extracting algorithm, then manipulate and extend them respectively according to their own personalities, and finally merge the newly synthesized pattern layer and material layer again to generate the final output. Experiment results show that our method not only greatly improves the synthesis quality for those cases that single-layer synthesis cannot handle well but also provides an ability of achieving various special synthesis effects.
In this paper,a dynamic flow-regulation algorithm-oriented network overload control is proposed.It can proportion-ally distribute the load between the high-degree nodes and the low-degree nodes.According to the theoretical analysis,the net-work transmission performance of the proposed algorithm is in inverse proportion to the usage rate of the high-degree nodes.Simulations show that the new algorithm is more flexible and can enhance the network capability in most circumstances compared with the shortest path routing algorithm.Moreover,the compari-son with the efficient routing algorism also reveals the prominent performance of the new algorithm.
MSU Stego Video is a public video steganographic tool, which has strong robustness and is regarded as a real video steganographic tool. In order to increase the detection rate, this paper proposes a new steganoalysis method against MSU, which uses the chessboard character of MSU embedded video, proposes a down-sample block-based collusion method to estimate the original frame and checks the chessboard mode of the different frame between tested frame and estimated frame to detect MSU steganographic evidences. To reduce the error introduced by severe movement of the video content, a method that abandons severe motion blocks from detecting is proposed. The experiment results show that the false negative rate of the proposed algorithm is lower than 5%, and the false positive rate is lower than 2%. Our algorithm has significantly better performance than existing algorithms. Especially to the video that has fast motion, the algorithm has more remarkable performance.
Under the global circumstances where data leakage gets more and more severe, we present a trustworthiness-based distribution model that aims at data leakage prevention （DLP）. In our model, first, the distributor calculates the user＇s trustworthiness based on his historical behaviors; second, according to the user＇s trustworthiness and his obtained file set overlapping leaked file set, the distributor accesses the probability of the user＇s intentional leak behavior as the subjective risk assessment; third, the distributor evaluates the user＇s platform vulnerability as an objective element; last, the distributor makes decisions whether to distribute the file based on the integrated risk assessment. The experiments indicate that the model can distinguish users of different types and make the probability of malicious users＇ requirements being denied much higher than that of honest users＇ requirements being denied, so that the model is capable of preventing data leakage validly.
