By Jing Tian, Li Chen
Due to expanding strength in real-world purposes comparable to visible communications, laptop assisted biomedical imaging, and video surveillance, picture and video interpretations became a space of transforming into interest.
Intelligent picture and Video Interpretation: Algorithms and purposes covers all facets of photo and video research from low-level early visions to high-level popularity. This e-book highlights how those options became appropriate and may end up to be a helpful software for researchers, pros, and graduate scholars operating or learning the fields of imaging and video processing.
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Additional info for Intelligent Image and Video Interpretation: Algorithms and Applications
According to spatial content of the video, feature extractor extracts the feature of the video. After feature extraction, watermark generator generates the watermark according to features and embeds the watermark into video. At the receiving site, video encoder extracts the watermark from the video. If this watermark matches the original watermark, the given video is claimed as authentic. However attacks on watermarks may not necessarily remove the watermark (Johnson, 1999), but can disable its readability.
In lighting approach, the direction of an illuminating light source for each object or person in an image is automatically evaluated by some mathematical techniques. The retouching technique exploits the technology by which a digital camera sensor records an image, for detecting a specific form of tampering. A robust video authentication system should tolerate the incidental distortion, which may be introduced by normal video processing such as compression, resolution conversion, and geometric transformation, while being capable of detecting the intentional distortion, which may be introduced by malicious attack.
Algorithm: Using steps 1-7 of the SVM learning algorithm, the statistical local information SL for the input video is computed. a. This statistical local information of the input video data is projected into the SVM hyper plane to classify the video as tampered or non-tampered. If the output of SVM is one for all of the difference frames of the input video then the given video is authentic otherwise it is tampered. b. If any of the frame of given video is classified as tampered then we determine the particular frames of the video that have been tampered.