Copy-paste forgery is a very common type of forgery in JPEG images.The tampered patch has always suffered from JPEG compression twice with inconsistent block segmentation.This phenomenon in JPEG image forgeries is called the shifted double JPEG(SDJPEG) compression.Detection of SDJPEG compressed image patches can make crucial contribution to detect and locate the tampered region.However,the existing SDJPEG compression tampering detection methods cannot achieve satisfactory results especially when the tampered region is small.In this paper,an effective SDJPEG compression tampering detection method utilizing both intra-block and inter-block correlations is proposed.Statistical artifacts are left by the SDJPEG compression among the magnitudes of JPEG quantized discrete cosine transform(DCT) coefficients.Firstly,difference 2D arrays,which describe the differences between the magnitudes of neighboring JPEG quantized DCT coefficients on the intrablock and inter-block,are used to enhance the SDJPEG compression artifacts.Then,the thresholding technique is used to deal with these difference 2D arrays for reducing computational cost.After that,co-occurrence matrix is used to model these difference 2D arrays so as to take advantage of second-order statistics.All elements of these co-occurrence matrices are served as features for SDJPEG compression tampering detection.Finally,support vector machine(SVM) classifier is employed to distinguish the SDJPEG compressed image patches from the single JPEG compressed image patches using the developed feature set.Experimental results demonstrate the efficiency of the proposed method.
In this paper,we propose a mathematical model of aggregate co-channel interference over Rayleigh fading in cognitive networks.Unlike the statistical models in the literature that aim at finding the bound or approximation of the interference,the proposed model gives an accurate expression of probability density function(PDF),cumulative distribution function(CDF) and mean and variance of the interference,which takes into account a number of factors,such as spectrum sensing scheme,and spatial distribution of the secondary users(SUs).In particular,we focus on a more general spatial structure where there are two roles of primary users(PUs)and the interfering SUs distributed in the two-dimensional space.The framework developed in this paper is easy to be applied in power control,error evaluation and other applications.
Active tamper detection using watermarking technique can localize the tampered area and recover the lost information. In this paper, we propose an approach that the watermark is robust to legitimate lossy compression, fragile to malicious tampering and capable of recovery. We embed the watermark bits in the direct current value of energy concentration transform domain coefficients. Let the original watermark bits be content dependent and apply error correction coding to them before embedded to the image. While indicating the tampered area, the extracted bits from a suspicious image can be further decoded and then used to roughly recover the corresponding area. We also theoretically study the image quality and bit error rate. Experimental results demonstrate the effectiveness of the proposed scheme.
This paper presents a dynamic probabilistic marking algorithm with multiple routing address tags, which allows the victim to traceback the origin of ICMP(Internet Control Message Protocol)-based direct and reflective DoS attacks. The proposed approach makes full use of scalable data space of ICMP packet to achieve multiple information tags. The difference between this proposal and previous proposals lies in two points. First, the number of packets needed by the victim to reconstruct the attack path is greatly reduced because of three key mechanisms: multi-tag, uniform leftover probability, and tag location choice based on the module of accommodated tag numbers within a packet. Second, the true origin of both direct and reflective ICMP-based DoS attacks can be traced.
CHEN XiuzhenMA JinLI ShenghongCHEN KenSERHROUCHNI Ahmed
