PPT-Forensic Detection of Image Manipulation Using Statistical

Fernando Barros Filipe Berti Gabriel Lopes Marcos Kobuchi Lab arararPr langen sz1200 b0 i0 unone strikenoStrike capnone baseline0 dirty0asolidFillaschemeClr valdk1asolidFillalatin typefaceCabinaea typefaceCabinacs typefaceCabinasym typefaceCabinarPr. State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. Author: Michael Sedivy. Introduction. Edge Detection in Image Processing. MCMC and the Use of Gibbs Sampler. Input. Results. Conclusion/Future Work. References. Edge Detection. Detecting Edges in images is a complex task, but it useful in other image processing problems. A Statistical Study and . Potential Software Tools For Practitioners . Carol . Gambino. , Patrick McLaughlin, Loretta . Kuo. , . Peter . Diaczuk. , Gerard . Petillo. , . Frani. . Kammerman. , Lauren . State-of-the-art face detection demo. (Courtesy . Boris . Babenko. ). Face detection and recognition. Detection. Recognition. “Sally”. Consumer application: Apple . iPhoto. http://www.apple.com/ilife/iphoto/. 2011/12/08. Robot Detection. Robot Detection. Better Localization and Tracking. No Collisions with others. Goal. Robust . Robot . Detection. Long . Range. Short. . Range. Long Range. C. urrent . M. ethod. Fernando Barros. Filipe Berti. Gabriel Lopes. Marcos Kobuchi. Lab. . As close to the truth as possible. Well regulated relations. Absolutely free will, based on thorough access to information.. Decietful manipulation seeks to skip particular elements of the definition above.. Mingchao Ma. STFC – RAL, UK. HEPSYSMAN Workshop. 10. th. June . 2010. Overview. Security incident handling lifecycle. Based . on . NIST SP800-61rev1 . recommendation. http://. csrc.nist.gov/publications/nistpubs/800-61-rev1/SP800-61rev1.pdf. Source: D. Lowe, L. Fei-Fei. Canny edge detector. Filter image with x, y derivatives of Gaussian . Find magnitude and orientation of gradient. Non-maximum suppression:. Thin multi-pixel wide “ridges” down to single pixel width. Colors represent nature of change (. e.g. ., more or less greenness, . etc.. ). USGS Image. 2011 Minimum. 2011 Sea ice extent. NASA Video. Change Detection in Remote Sensing. Using multi-temporal satellite imagery to measure change. Statistical Features for Image Splicing Detection. Xudong. Zhao, . Shilin. Wang, . Shenghong. Li and . Jianhua. Li. Shanghai Jiao Tong University, Shanghai P. R. China. Introduction. Digital Image Forensics:. Fig.1: Retinal image Different techniques are given in literature for macula detection. In [6], macula is detected using morphological properties of eye. [7] Presents the methodology to detect macula Ales . Zita. Publication. Digital Image Forgery Detection Based on Lens and Sensor Aberration. Authors. . : . Ido. . Yerushalmy. , . Hagit. Hel-Or. . Dept. of Computer Science, University of Haifa, Israel. Applications. 报告人：程明明. 南开大学、计算机与控制工程学. 院. http://mmcheng.net/. Contents. Global . contrast based salient region . detection. ,. PAMI 2014. BING: Binarized Normed Gradients for Objectness Estimation at .