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As the number of Deepfake video contents grows rapidly, automatic Deepfake detection has received a lot of attention in the community of digital forensics. Deepfake videos can be potentially harmful to society, from non-consensual explicit content creation to forged media by foreign adversaries used in disinformation campaigns.

A light-weight high-performance Deepfake detection method, called DefakeHop, is proposed in this work. State-of-the-art Deepfake detection methods are built upon deep neural networks. DefakeHop extracts features automatically using the successive subspace learning (SSL) principle from various parts of face images. DefakeHop consists of three main modules: 1) PixelHop++, 2) feature distillation and 3) ensemble classification. To derive the rich feature representation of faces, DefakeHop extracts features using PixelHop++ units from various parts of face images. The theory of PixelHop++ have been developed by Kuo et al. using SSL. PixelHop++ has been recently used for feature learning from low-resolution face images but, to the best of our knowledge, this is the first time that it is used for feature learning from patches extracted from high-resolution color face images. Since features extracted by PixelHop++ are still not concise enough for classification, we also propose an effective feature distillation module to further reduce the feature dimension and derive a more concise description of the face. Our feature distillation module uses spatial dimension reduction to remove spatial correlation in a face and a soft classifier to include semantic meaning for each channel. Using this module the feature dimension is significantly reduced and only the most important information is kept. With a small model size of 42,845 parameters, DefakeHop achieves state-of-the-art performance with the area under the ROC curve (AUC) of 100%, 94.95%, and 90.56% on UADFV, Celeb-DF v1 and Celeb-DF v2 datasets, [...]

We have a new member, Jiahui Zhang, joining MCL in Spring 2021. Here is a short interview with Jiazhi with our great welcome.

1. Could you briefly introduce yourself and your research interests?

My name is Jiahui Zhang, I am a second-year master student in the Department of Electronic Engineering in USC. I got my bachelor degree from Beijing University of Technology. I am a sports fan. In my spare time, I like playing sports and watching sports games. I also like traveling to view good scenery. My research interests include deep learning, computer vision especially representation learning.

2. What is your impression about MCL and USC?

USC is a great school that could provide student a enjoyable environment on living, communicating and studying.

MCL is a wonderful lab filled with a number of intelligent researchers. Everyone is an expert in their research field. Besides, people in MCL lab from Professor Kuo to every lab member are very kind and friendly. People help each other on living, studying, and researching, which build a warm environment in the lab.

3. What is your future expectation and plan in MCL?

MCL has many great and excellent researchers, and I want to study and make friends with them. For academic, I want to accomplish some projects to accumulate my research experiences and make contribution to the lab.

Research related to the future development of Health Care systems is always a significant endeavor, by touching many people lives. AI advancements in the last decade have given rise to new applications, with key aim to increase the automation level of different tasks, currently being carried out by experts. In particular, medical image analysis is a fast-growing area, having also been revolutionized by modern AI algorithms for visual content understanding. Magnetic Resonance Imaging (MRI) is widely used by radiologists in order to shed more light on patient’s health situation. It can provide useful cues to experts, thus assisting to take decisions about the appropriate treatment plan, maintaining also less discomfort for the patient and incurring less economical risks in the treatment process.

The question arises, how modern AI could contribute to automate the diagnosis process and provide a second and more objective assessment opinion to the experts. Many research ideas from the visual understanding area, adopt the deep learning (DL) paradigm, by training Deep Neural Networks (DNNs) to learn end-to-end representations for tumor classification, lesion areas detection, specific organ segmentation, survival prediction etc. Yet, one could identify some limitations on using DNNs in medical image analysis. It is well known that it is often hard to collect sufficient real samples for training DL models. Furthermore, decisions made by machines need to be transparent to physicians and especially be aware of the factors that led to those decisions, so that they are more trustworthy. DNNs are often perceived as “black-box” models, since their feature representations and decision paths are hard to be interpreted.

In MCL, we consider a new line of research on AI for medical image analysis, by adopting the Green Learning (GL) approach to address [...]

Sustainability has become a main theme of science and technology in recent years. As civilization continues to develop, humans need be conscious in keeping the environment clean for future generations. As scientists and engineers of the 21st century, it is our destiny to keep green technologies as one of the top priorities. In the area of artificial intelligence and machine learning, it is urgent to explore a novel green machine learning technology, which is competitive with deep learning in performance yet with significantly lower power consumption in training and inference.

Green learning will be the central focus of the USC Media Communications Lab (MCL) in the next decade. Professor Kuo, Director of MCL, has been devoted to this subject since 2015. A sequence of papers on green learning systems has been published. Examples include: PixelHop, PointHop, FaceHop, GraphHop, GenHop, etc. These solutions have common characteristics, including low power consumption, small model sizes, weak supervision and scalability. The underlying principle of MCL’s green learning solutions is successive subspace learning (SSL).

MCL will continue to push the envelope of green learning and develop effective green solutions for natural language processing, knowledge understanding, computer vision, joint audio-visual processing, and 3D data processing.

Congratulations to MCL Director, Professor C.-C. Jay Kuo, for being elected as a Fellow of the National Academy of Inventors (NAI). The announcement was made by the NAI President, Dr. Paul R. Sanberg, on December 8.

This year’s class includes three professors at the USC Viterbi School of Engineering: Gerald Loeb, professor of biomedical engineering and neurology; Keith Chugg, professor of electrical and computer engineering; and Jay Kuo, distinguished professor of electrical and computer engineering and computer science.

The 2020 NAI Fellow class has 175 academic innovators from across the world. It represents 115 research universities and governmental and non-profit research institutes worldwide. They collectively hold over 4,700 issued U.S. patents. Among the 2020 Fellows are recipients of the National Academies of Sciences, Engineering, and Medicine, American Academy of Arts & Sciences, and Nobel Prize, as well as other honors and distinctions. Their collective body of research covers a range of scientific disciplines including biomedical engineering, computer engineering, materials science, and physics.

With the election of the 2020 class, there are now 1,403 NAI Fellows worldwide, representing more than 250 prestigious universities and governmental and non-profit research institutes. To date, NAI Fellows hold more than 42,700 issued U.S. patents, which have generated over 13,000 licensed technologies and companies, and created more than 36 million jobs. In addition, over $2.2 trillion in revenue has been generated based on NAI Fellow discoveries.