Cyber Security

Image and Video Forensics

Mayachitra team is developing state-of-the-art methods for media forensics (supported by the DARPA MediFOR program, and in collaboration with researchers at the China Lake Air Warfare Research Center and the University of California, Riverside). The developed methods will be able to differentiate between manipulated and un-manipulated images, and localize the manipulated regions. These methods leverage recent advances in deep learning for image analysis with the team’s expertise in image processing, segmentation and classification.

Recent Publication(s):
  • Michael Goebel, Jason Bunk, Srinjoy Chattopadhyay, Lakshmanan Nataraj, Shivkumar Chandrasekaran, B. S. Manjunath, "Attribution of Gradient Based Adversarial Attacks for Reverse Engineering of Deceptions", IS&T International Symposium on Electronic Imaging, 2021 [paper]
  • Lakshmanan Nataraj, Michael Goebel, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, B. S. Manjunath, "Holistic Image Manipulation Detection using Pixel Co-occurrence Matrices", IS&T International Symposium on Electronic Imaging, 2021 [paper]
  • Michael Goebel, Lakshmanan Nataraj, Tejaswi Nanjundaswamy, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, B. S. Manjunath, "Holistic Image Manipulation Detection using Pixel Co-occurrence Matrices", IS&T International Symposium on Electronic Imaging, 2021 [paper]
  • Jawadul H. Bappy, Cody Simons, Lakshmanan Nataraj, Arjuna Flenner, B. S. Manjunath, Amit K. Roy-Chowdhury, "Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries", IEEE Transactions on Image Processing (TIP), 2019 [paper]
  • Lakshmanan Nataraj, Chandrakanth Gudavalli, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, B. S. Manjunath, "Seam Carving Detection and Localization Using Two-Stage Deep Neural Networks", Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication (MDCWC), 2020 [paper]
  • Lakshmanan Nataraj, Tajuddin Manhar Mohammed, B. S. Manjunath, Shivkumar Chandrasekaran, Arjuna Flenner, Amit K. Roy-Chowdhury, "Detecting GAN generated Fake Images using Co-occurrence Matrices", IS&T International Symposium on Electronic Imaging, 2019 [paper]
  • Michael Goebel, Arjuna Flenner, Lakshmanan Nataraj, B. S. Manjunath, "Deep Learning Methods for Event Verification and Image Repurposing Detection", IS&T International Symposium on Electronic Imaging, 2019 [paper]
  • Tajuddin Manhar Mohammed, Jason Bunk, Lakshmanan Nataraj, Jawadul H. Bappy, Arjuna Flenner, B. S. Manjunath, Shivkumar Chandrasekaran, Anit K. Roy-Chowdhury, Lawrence Peterson, "Boosting Image Forgery Detection using Resampling Detection and Copy-move analysis", IS&T International Symposium on Electronic Imaging, 2018 [paper]
  • Arjuna Flenner, Lawrence Peterson, Jason Bunk, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, B. S. Manjunath, "Resampling Forgery Detection using Deep Learning and A-contrario analysis", IS&T International Symposium on Electronic Imaging, 2018 [paper]
  • Jawadul H. Bappy, Amit K. Roy-Chowdhury, Jason Bunk, Lakshmanan Nataraj, B. S. Manjunath, "Exploiting Spatial Structure for Localizing Manipulated Image Regions", IEEE International Conference on Computer Vision (ICCV), 2017 [paper]
  • Jason Bunk, Jawadul H. Bappy, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Arjuna Flenner, B. S. Manjunath, Shivkumar Chandrasekaran, Amit K. Roy-Chowdhury, Lawrence Peterson, "Detection and Localization of Image Forgeries using Resampling Features and Deep Learning", IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 [paper]

New approaches for Malware Detection and Classification

Researchers at Mayachitra are pioneering new ways of detecting and classifying computer malware. A new malware detection/classification system, called MalSee, is deployed as a web-accessible service. Mayachitra’s unique technology empowers robust and fast detection of harmful computer viruses, offering ~1000x speedup compared to existing methods. The solution leverages on ideas from signal processing, pattern recognition, and deep learning. This project is currently being supported by a Phase II Navy SBIR funding. The developed algorithms utilizes resources and malware samples from various sources including UCSB Vision Research Lab, UCSB Seclab, VirusShare, VirusTotal and VirusSamples.

Recent Publication(s):
  • Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar, Shivkumar Chandrasekaran, B. S. Manjunath, "MalGrid: Visualization Of Binary Features In Large Malware Corpora", IEEE Military Communications Conference (MILCOM), 2022. [paper]
  • Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar, Shivkumar Chandrasekaran, B. S. Manjunath, "Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning", Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS), 2021 [paper]
  • Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar, Shivkumar Chandrasekaran, B. S. Manjunath, "HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis", IEEE Military Communications Conference (MILCOM), 2021 [paper]
  • Lakshmanan Nataraj, Tajuddin Manhar Mohammed, Tejaswi Nanjundaswamy, Satish Chikkagoudar, Shivkumar Chandrasekaran, B. S. Manjunath, "OMD: Orthogonal Malware Detection Using Audio, Image, and Static Features", IEEE Military Communications Conference (MILCOM), 2021 [paper]
  • Lakshmanan Nataraj, B. S. Manjunath, "SPAM: Signal Processing to Analyze Malware", IEEE SIgnal ProcESSIng MagazInE, 2016 [paper]