Machine Learning Techniques for Image Forensics in Adversarial Setting

The use of machine-learning for multimedia forensics is gaining more and more consensus, especially due to the amazing possibilities offered by modern machine learning techniques. By exploiting deep learning tools, new approaches have been proposed whose performance remarkably exceed those achieved by state-of-the-art methods based on standard machine-learning and model-based techniques. However, the inherent vulnerability and fragility of machine learning architectures pose new serious security threats, hindering the use of these tools in security-oriented applications, and, among them, multimedia forensics. The analysis of the security of machine learning-based techniques in the presence of an adversary attempting to impede the forensic analysis, and the development of new solutions capable to improve the security of such techniques is then of primary importance, and, recently, has marked the birth of a new discipline, named Adversarial Machine Learning. By focusing on Image Forensics and image manipulation detection in particular, this thesis contributes to the above mission by developing novel techniques for enhancing the security of binary manipulation detectors based on machine learning in several adversarial scenarios. The validity of the proposed solutions has been assessed by considering several manipulation tasks, ranging from the detection of double compression and contrast adjustment to the detection of geometric transformations and filtering operations.

About the Author:

Ehsan Nowroozi, the author of this thesis, was born on the 10th of June 1986 in Iran. He grew up in Shiraz city is one of the most beautiful, historical cities in the world. Farsi (Persian or Parsi) the language of Ancient Fars (Pars), has become the official language of Iran (Persia). The first Capital of Fars, some 2500 years ago, was Pasargad. It was also the capital of Achaemenid King Cyrus the Great. His doctoral research investigates in Multimedia Forensics, with particular reference to Machine learning techniques for image forensics in an adversarial setting. He holds a master's degree in Computer Engineering - Computer Architecture from the Shahid Beheshti University (SBU), Tehran, Iran, that investigated Double JPEG compression detection using statistical analysis. He graduated as the first rank during his M.Sc with a GPA of 17.95 out of 20. In October 2016, he was selected as a Ph.D. student with the scholarship from the University of Siena, Italy in information engineering and mathematical sciences, working in the Visual Information Processing and Protection (VIPP) Lab under the supervision of Professor Mauro Barni. He wins one research scholarships, which supported by Defense Advanced Research Projects Agency (DARPA) and US Airforce Laboratory, USA. He has published various papers and a couple of books (Persian language) in the eld of multimedia forensics. Also, he has been working on theoretical and practical aspects of adversarial multimedia forensics and adversarial machine learning with particular reference to the application of image processing techniques to authentication of multimedia (multimedia forensics).

Author: Ehsan Nowroozi
University of Siena
Theses
Contact: ehsan.nowroozi@diism.unisi.it