Congratulations to Yao Zhu for Passing Her Defense

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 [...]

By |June 18th, 2023|News|Comments Off on Congratulations to Yao Zhu for Passing Her Defense|
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    MCL member Vasileios Magoulianitis presented in the ICASSP 2023 conference

MCL member Vasileios Magoulianitis presented in the ICASSP 2023 conference

Vasileios had a trip to Greece to attend ICASSP 2023 and present two posters of MCL. Let’s hear what he would like to share about his experience:

ICASSP this year was held on the island of Rhodes in Greece which is popular touristic summer destination. The venue hosted a quite large number of presentations this year -most of them as posters-, interesting keynote speeches and other IEEE community side events, such as the celebration of Signal Processing Society (SPS) 75th anniversary.

I presented two works of our lab in form of posters, the one on rotation invariant 3D Point cloud classification, proposed by Pranav Kadam et al. and the new classification method from our lab, named  SLM (Classification via Subspace Learning Machine), proposed by Hongyu Fu. Most of the individuals that stopped by the posters, commented on the fact that our works stood out from the rest deep learning approaches, as they have more intuition and transparency, as well as offering a lightweight solution, which was especially appealing to some industry representatives.

Peripheral to the ICASSP venue, other non-technical activities took place, such as traditional cooking or Greek dancing lessons, and some festival nights with live music bands.

— Vasileios Magoulianitis

By |June 11th, 2023|News|Comments Off on MCL member Vasileios Magoulianitis presented in the ICASSP 2023 conference|

MCL Research on Camouflage Object Detection

Camouflage is an attempt to mask the object into a background image and to match its background. The term “camouflage” originates in the ancient practices of the animal kingdom, where animals would alter their body patterns, textures, and colors to blend in with their environment to evade predators. In military contexts, camouflage is a technique used to conceal soldiers or equipment within the background texture, making it difficult for the enemy to detect them. On the other hand, camouflaged object detection is a method used to uncover the enemy who has used camouflage to hide within the image texture.

Our research objectives are to understand the problem with more insights and develop a pipeline for Camouflaged Object Detection based on the Green Learning framework. The first main technical accomplishments are multiscale color and texture decomposition; since, in this task, the texture is much more important than the color, we decouple the information of textures and colors. The second main technical accomplishment is clustering the images dataset; we cluster similar photos with the matching color histogram together and try to learn the information. It reduces the difficulty of the classifier to separate all kinds of images, and we can focus more on images with similar colors and appearance. We cluster the dataset using HSV color space, quantize the color space into 52 bins, and use K-means clustering to derive the clusters. As shown in Figure, images with similar backgrounds are grouped together. Our experiment shows that training in each subcluster could further reduce training and validation loss.

— Max Chen

By |June 4th, 2023|News|Comments Off on MCL Research on Camouflage Object Detection|
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    MCL member Ganning Zhao presented in the SPIE DCS conference

MCL member Ganning Zhao presented in the SPIE DCS conference

Ganning had a trip to Orlando to participate in the SPIE DCS conference. Let’s hear what she would like to share about her work:

Ensuring the realism of computer-generated synthetic images is crucial to deep neural network (DNN) training. Due to different semantic distributions between synthetic and real-world captured datasets, there exists semantic mismatch between synthetic and refined images, which in turn results in the semantic distortion. Recently, contrastive learning (CL) has been successfully used to pull correlated patches together and push uncorrelated ones apart. In this work, we exploit semantic and structural consistency between synthetic and refined images and adopt CL to reduce the semantic distortion. Besides, we incorporate hard negative mining to improve the performance furthermore. We compare the performance of our method with several other benchmarking methods using qualitative and quantitative measures and show that our method overs the state-of-the-art performance.

— Ganning Zhao

By |May 28th, 2023|News|Comments Off on MCL member Ganning Zhao presented in the SPIE DCS conference|

MCL Genealogical Ancestry Series

Martin Ohm (May 6, 1792 in Erlangen – April 1, 1872 in Berlin) was a German mathematician and a younger brother of physicist Georg Ohm. He earned his doctorate in 1811 at Friedrich-Alexander-University, Erlangen-Nuremberg where his advisor was Karl Christian von Langsdorf. In 1817, he was appointed professor of mathematics and physics in the gymnasium at Thorn. In 1821 he moved to Berlin, and in 1839 became a full professor in the University of Berlin. He delivered courses of lectures at the academy of architecture from 1824 to 1831, and at the schools of artillery and engineering from 1833 to 1852; and he also taught in the military school from 1826 to 1849. Ohm was the first to fully develop the theory of the exponential ab when both a and b are complex numbers in 1823. The 1835 second edition of Ohm’s textbook, Die reine Elementar Mathematik was the first time that Euclid’s ‘extreme and mean ratio’ was given the name of the “golden section” (goldener Schnitt). Ohm’s notable students included Rudolf Lipschitz.

— Xiou Ge


By |May 21st, 2023|News|Comments Off on MCL Genealogical Ancestry Series|
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    Congratulations to MCL Members in Attending PhD Hooding Ceremony

Congratulations to MCL Members in Attending PhD Hooding Ceremony

Ten MCL members attended the Viterbi PhD hooding ceremony on Wednesday, May 10, 2023, in the Bovard Auditorium. They were Pranav Kadam, Xiou Ge, Yao Zhu, Xuejing Lei, Zohreh Azizi, Zhiruo Zhou, Yun Cheng Wang, Yifan Wang, Hongyu Fu, Hong-Shuo Chen. Congratulations to them for their accomplishments in completing their PhD program at USC!

Pranav Kadam received his Master’s degree in Electrical Engineering from USC in 2020 and Bachelor’s degree from Savitribai Phule Pune University, India in 2018. He joined Media Communications Lab in Summer 2019 guided by Prof. C.-C. Jay Kuo. His research interests include 3D Computer Vision and Machine Learning. His thesis is titled “Green Learning for 3D Point Cloud Data Processing”. He will join Tencent America.

Xiou Ge received the B.S. and M.S. degree in electrical and computer engineering from University of Illinois Urbana-Champaign, Urbana, Illinois, in 2016 and 2018 respectively. His research is on knowledge graph embedding and its applications in natural language processing. He is a recipient of USC Annenberg Graduate Fellowship (2019). His thesis is titled “Advanced Knowledge Graph Embedding Techniques: Theory and Applications”. He will join Apple’s AI/ML research team.

Yao Zhu received her Bachelor’s degree in Central South University in Changsha, Hunan, China. She joined Media Communication Lab leading by Professor Kuo in summer 2018. Her research interests include Image Synthesis, 3D construction and Computer Vision.

Xuejing Lei received her Bachelor’s degree in Automation from Xi’an Jiaotong University, China in June 2016, and received her Master’s degree in Electrical Engineering from USC in May 2018. She joined Media Communications Lab in 2017 summer. Her research interests include computer vision and deep learning.

Zohreh Azizi received the B.S. degree in Electrical Engineering from Sharif University of Technology, Iran, in 2018. She joined [...]

By |May 14th, 2023|News|Comments Off on Congratulations to MCL Members in Attending PhD Hooding Ceremony|

Congratulations to Xiou Ge for Passing His Defense

Let us hear what he has to say about his defense and an abstract of his thesis.

Knowledge graphs (KGs) find rich applications in knowledge management and discovery, recommendation systems, fraud detection, chatbots, etc. KGs are directed relational graphs. They are formed by a collection of triples in the form of (head, relation, tail), where heads and tails are called entities and represented by nodes while relations are links in KGs. Since KGs are often incomplete, one critical task in KG is to predict missing information based on available knowledge. Knowledge graph embedding (KGE) is one of the most popular approaches for link prediction and entity typing due to its effectiveness, efficiency, and explainability.

In this thesis, we propose a KGE inspired by compounding three-dimensional geometric transformation named CompoundE3D. Specifically, we apply an adapted beam search algorithm to discover the best performing variants and experiment with different ensemble strategies to achieve state-of-the-art results on four benchmarking datasets. We also analyze the properties of different operators under the CompoundE3D framework and empirically verify their advantages.

I’d like to thank Prof. Kuo for his advising throughout the last 4 years. The working style in MCL has been very effective. Without the weekly reports and meetings, it would probably take me more time to grow. I really respect Prof. Kuo’s research vision and the research direction we embark on will be appreciated in the long term. I’m also very fortunate to be with a group of very nice fellow students in the lab. Everyone is so supportive and willing to help each other. MCL is a perfect fit for students like me who may not be the most talented but are determined to work hard and do good research.

— Xiou [...]

By |May 7th, 2023|News|Comments Off on Congratulations to Xiou Ge for Passing His Defense|

MCL Research on Advanced Object Tracking Technology

Visual tracking is an important task in computer vision and has been integrated into many applications such as autonomous vehicles. Single object tracking (SOT) is the fundamental problem of visual tracking where the tracking is performed on one specific object in the testing video. The ground truth bounding box is provided in the initial frame and the tracker needs to track the object location in all later frames. There could be various challenges such as occlusion, fast motion, viewpoint change, background clutters and so on. The SOT problem has been investigated for a long time, and supervised trackers that are trained offline with large scale labeled data dominate the leader board. The model complexity and computational cost of those large deep neural networks are unaffordable for edge devices. In recent years, unsupervised learning from unlabeled data and lightweight structures start to attract the attention of researchers.

Aiming at lightweight tracking with low computational complexity, we proposed a tracker that learns from the testing sequence per se and does not require any offline training, which further extended the boundary of tracking with low level vision features with comparable performance with recent unsupervised state-of-the-art deep trackers. The number of parameters and the required flops during inference time are much smaller than those of deep trackers. Some sample results are provided in the figures above. Our tracker is able to produce flexible bounding boxes and goes back to the object after occlusion or disappearance. We hope that this work can contribute to the innovations of lightweight tracking and help with better understanding of the roles of feature representations and offline training.

–Zhiruo Zhou

By |April 30th, 2023|News|Comments Off on MCL Research on Advanced Object Tracking Technology|

MCL Research on Green Image Steganalysis

Steganography for spatial images is a technique that involves hiding secret information within digital images in a way that is imperceptible to the human eye. It considers the characteristics and features of the cover media in a local region, and embed the secret information in a manner that is visually or statistically inconspicuous.

As the other side of the coin, steganalysis, is the way of detecting the embedded image (often we call it stego image). There has been numerous works in detecting the hidden information. Traditionally, people use heuristic features and ensemble of machine learning models for detection. It soon becomes feeble in detecting content adaptive steganographic images. After the emerging of neural networks, researchers start to use deep models to detect the weak stego signals by extracting features and doing classification in a whole. Different from the traditional steganalysis and deep learning based steganalysis methods, we propose a novel steganalysis scheme, which is a green steganalysis method.

We first scatter the whole image into small blocks, and then perform anomaly detection on block levels. This step will give us an indication of the likelihood such that this block is embedded or not. Next, we train an embed location detector, to help us locate the blocks that are more discriminant than others. Finally, blocks that are selected from previous step will be fused together and make image-level decision by ensemble classifier. Our architecture is completely explainable, and computationally efficient.

— by Yao Zhu

By |April 22nd, 2023|News|Comments Off on MCL Research on Green Image Steganalysis|
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    MCL Genealogical Ancestry Series – Abraham Gotthelf Kastner

MCL Genealogical Ancestry Series – Abraham Gotthelf Kastner

Abraham Gotthelf Kastner (27 September 1719 – 20 June 1800) was a German mathematician and epigrammatist.

Kästner was the son of law professor Abraham Kästner. Starting from 1731 (aged 12), he studied law, philosophy, physics, mathematics and metaphysics in Leipzig. He was appointed a Notary in 1733, when he was 14. He gained his habilitation (PhD degree in Europe) from the University of Leipzig in 1739 at age 20.  In his early careers, he lectured mathematics, philosophy, logic and law in University of Leipzig after his habilitation. He soon became an associate professor in 1746, at age 27. In 1751 he was elected a member of the Royal Swedish Academy of Sciences, which have famous fellows such as Isaac Newton, Charles Darwin, Alan Turing, Stephen Hawking, etc. In 1756, at his 37, he took up a position as full professor of natural philosophy and geometry at the University of Göttingen. His notable doctoral students include Johann Pfaff, who is the doctoral advisor of Carl Friedrich Gauss). Kästner died in 1800 in Göttingen, at age 81.

Kästner has numerous mathematical writings, including Anfangsgründe der Mathematik (“Foundations of Mathematics”) (Göttingen 1758-69, 4 volumes; 6th edition 1800) and Geschichte der Mathematik (“History of Mathematics”) (Göttingen 1796-1800, 4 volumes). Geschichte der Mathematik is considered an astute work, but lacks a comprehensive overview of all subsections of mathematics. Besides his contribution in math, he is more well-known for his poems, which were notable for their biting humour and sharp irony on different contemporary personalities.

As his descendants, we know Kästner as a talented scholar and devoted researcher in mathematics, philosophy, logic and law. His rich contributions shall be remembered the same as himself.

By |April 16th, 2023|News|Comments Off on MCL Genealogical Ancestry Series – Abraham Gotthelf Kastner|