Congratulations to Yao Zhu for passing her defense on June 12, 2023. Yao’s thesis is titled “A Green Learning Approach to Image Forensics: Methodology, Applications, and Performance Evaluation.” Her Dissertation Committee includes Jay Kuo (Chair), Antonio Ortega, and Jernej Barbic (Outside Member). Yao received several questions and suggestions from the Committee members. Yao answered the questions professionally.

Congratulations to Yao for this milestone moment in life. MCL News team invited Yao for a short talk on her thesis and PhD experience, and here is the summary. We thank Yao for her kind sharing, and wish her all the best in the next journey.

Fake images have become a central problem in the last few years, especially after the advent of neural networks. Fake images are usually created by whole generation, partial tampering or information hiding. Image forensics, on the contrary, aims to detect the fake contents or discover the hidden information from fake objects. It leverages the fact that manipulation actions leave detectable traces, making fake images statistically distinguishable from genuine ones.

I specifically talked about two long-standing problems in image forensics: GAN- generated image detection and spatial image steganalysis. The former one aims to detect images that are synthesized by generative models. The latter one focus on distinguishing stego and cover images in spatial domain, where stego images are generated by various content-adaptive steganography algorithms. The stego signal that are embedded into cover images is so weak that the difference in pixel domain in only +1 or -1. The solutions that we propose to these two problems are both ‘green’ solutions, which have significantly small model sizes and computational cost. In the meantime, our methods are mathematically transparent due to the modularized design. Green Learning has been proved to have promising potential to the image forensics area. Future topics such as image splicing location were also discussed.

I would like to especially thank Professor Kuo. I won’t make it to this end without his help and support. I appreciate all the guidance, patience and trust throughout my PhD journey. Also, I’d like to thank all the MCL members. It’s been a pleasure to go through this journey with the companion of such friendly, supportive, and talented peers. Professor Kuo always said, PhD is a strenuous journey. During this journey, I became more persistent, humble and motivated. I wish all the lab members can successfully make it to the end. Stay healthy, be well, and good luck! ”