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Manipulated digital image is got interesting in recent years. Digital images can be manipulated more easily with the aid of powerful image editing software. Forensic techniques for authenticating the integrity of digital images and exposing forgeries are urgently needed. A geometric-based forensic technique which exploits the principle of vanishing points is proposed. By means of edge detection and straight lines extraction, intersection points of the projected parallel lines are computed. The normalized mean value (NMV) and normalized standard deviation (NSD) of the distances between the intersection points are used as evidence for image forensics. The proposed method employs basic rules of linear perspective projection, and makes minimal assumption. The only requirement is that the parallel lines are contained in the image. Unlike other forensic techniques which are based on low-level statistics, this method is less sensitive to image operations that do not alter image content, such as image resampling, color manipulation, and lossy compression. This method is demonstrated with images from York Urban database. It shows that the proposed method has a definite advantage at separating authentic and forged images.

In order to identify P2P traffic quickly and accurately as early as possible, an early intelligent P2P traffic identification method(EIIC) is proposed, which uses the size of early three packets payload and server port number obtained from the TCP flow as flow feature and classifies the traffic based on C4.5 algorithm. The results show that EIIC satisfies the following conditions: extracted features used, training samples selected under the unbiased conditions, it can be adaptive to actual network conditions and early classify the Internet traffic into application among WEB, MAIL, BitTorrent and eMule categories efficiently and quickly.