Small or smooth cloned regions are difficult to be detected in image copy-move forgery （CMF） detection. Aiming at this problem, an effective method based on image segmentation and swarm intelligent （SI） algorithm is proposed. This method segments image into small nonoverlapping blocks. A calculation of smooth degree is given for each block. Test image is segmented into independent layers according to the smooth degree. SI algorithm is applied in finding the optimal detection parameters for each layer. These parameters are used to detect each layer by scale invariant features transform （SIFT）-based scheme, which can locate a mass of keypoints. The experimental results prove the good performance of the proposed method, which is effective to identify the CMF image with small or smooth cloned region.
Subjective logic provides a means to describe the trust relationship of the realworld. However, existing fusion operations it offers Weal fused opiniotts equally, which makes it impossible to deal with the weighted opinions effectively. A. Jcsang presents a solution, which combines the discounting operator and the fusion operator to produce the consensus to the problem. In this paper, we prove that this approach is unsuitable to deal with the weighted opinions because it increases the uncertainty of the consensus. To address the problem, we propose two novel fusion operators that are capable of fusing opinions according to the weight of opinion in a fair way, and one of the strengths of them is improving the trust expressiveness of subjective logic. Furthermore, we present the justification on their definitions with the mapping between the evidence space and the opinion space. Comparisons between existing operators and the ones we proposed show the effectiveness of our new fusion operations.
ZHOU Hongwei1,2,3,SHI Wenchang1,2,LIANG Zhaohui1,2,LIANG Bin1,2 1.Key Laboratory of Data Engineering and Knowledge Engineering,Ministry of Education,Beijing 100872,China
In order to enhance the security of a browser password manager, we propose an approach based on a hardware trusted platform module （TPM）. Our approach encrypts users＇ passwords with keys generated by the TPM, which uses a master password as the credential for authorization to access the TPM. Such a hardware-based feature may provide an efficient way to protect users＇ passwords. Experiment and evaluation results show that our approach performs well to defend against password stealing attack and brute force attack. Attackers cannot get passwords directly from the browser, therefore they will spend incredible time to obtain passwords. Besides, performance cost induced by our approach is acceptable.Abstract： In order to enhance the security of a browser password manager, we propose an approach based on a hardware trusted platform module （TPM）. Our approach encrypts users＇ passwords with keys generated by the TPM, which uses a master password as the credential for authorization to access the TPM. Such a hardware-based feature may provide an efficient way to protect users＇ passwords. Experiment and evaluation results show that our approach performs well to defend against password stealing attack and brute force attack. Attackers cannot get passwords directly from the browser, therefore they will spend incredible time to obtain passwords. Besides, performance cost induced by our approach is acceptable.
Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance.However,for some CMF images,these approaches cannot produce satisfactory detection results.For instance,the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result.Sometimes these approaches may even produce error results.According to our observations,one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences.These values are only applicable to a few images,which limits their application.To solve the problem,a novel approach named as CMF Detection with Particle Swarm Optimization(CMFDPSO) is proposed in this paper.CMFD-PSO integrates the Particle Swarm Optimization(PSO) algorithm into the SIFT-based framework.It utilizes the PSO algorithm to generate customized parameter values for images,which are used for CMF detection under the SIFT-based framework.Experimental results show that CMFD-PSO has good performance.
