CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks

The unprecedented increase in the usage of computer vision technology in society goes hand in hand with an increased concern in data privacy. In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity. We propose and develop CIAGAN, a model for image and video anonymization based on conditional generative adversarial networks. Our model is able to remove the identifying characteristics of faces and bodies while producing high-quality images and videos that can be used for any computer vision task, such as detection or tracking. Unlike previous methods, we have full control over the de-identification (anonymization) procedure, ensuring both anonymization as well as diversity. We compare our method to several baselines and achieve state-of-the-art results.Comment: CVPR 202

SPIRE Map-Making Test Report

The photometer section of SPIRE is one of the key instruments on board of Herschel. Its legacy depends very much on how well the scanmap observations that it carried out during the Herschel mission can be converted to high quality maps. In order to have a comprehensive assessment on the current status of SPIRE map-making, as well as to provide guidance for future development of the SPIRE scan-map data reduction pipeline, we carried out a test campaign on SPIRE map-making. In this report, we present results of the tests in this campaign.Comment: This document has an executive summary, 6 chapters, and 102 pages. More information can be found at: https://nhscsci.ipac.caltech.edu/sc/index.php/Spire/SPIREMap-MakingTest201

Smart Road Danger Detection and Warning

Road dangers have caused numerous accidents, thus detecting them and warning users are critical to improving traffic safety. However, it is challenging to recognize road dangers from numerous normal data and warn road users due to cluttered real-world backgrounds, ever-changing road danger appearances, high intra-class differences, limited data for one party, and high privacy leakage risk of sensitive information. To address these challenges, in this thesis, three novel road danger detection and warning frameworks are proposed to improve the performance of real-time road danger prediction and notification in challenging real-world environments in four main aspects, i.e., accuracy, latency, communication efficiency, and privacy. Firstly, many existing road danger detection systems mainly process data on clouds. However, they cannot warn users timely about road dangers due to long distances. Meanwhile, supervised machine learning algorithms are usually used in these systems requiring large and precisely labeled datasets to perform well. The EcRD is proposed to improve latency and reduce labeling cost, which is an Edge-cloud-based Road Damage detection and warning framework that leverages the fast-responding advantage of edges and the large storage and computation resources advantages of the cloud. In EcRD, a simple yet efficient road segmentation algorithm is introduced for fast and accurate road area detection by filtering out noisy backgrounds. Additionally, a light-weighted road damage detector is developed based on Gray Level Co-occurrence Matrix (GLCM) features on edges for rapid hazardous road damage detection and warning. Further, a multi-types road damage detection model is proposed for long-term road management on the cloud, embedded with a novel image-label generator based on Cycle-Consistent Adversarial Networks, which automatically generates images with corresponding labels to improve road damage detection accuracy further. EcRD achieves 91.96% accuracy with only 0.0043s latency, which is around 579 times faster than cloud-based approaches without affecting users' experience while requiring very low storage and labeling cost. Secondly, although EcRD relieves the problem of high latency by edge computing techniques, road users can only achieve warnings of hazardous road damages within a small area due to the limited communication range of edges. Besides, untrusted edges might misuse users' personal information. A novel FedRD named FedRD is developed to improve the coverage range of warning information and protect data privacy. In FedRD, a new hazardous road damage detection model is proposed leveraging the advantages of feature fusion. A novel adaptive federated learning strategy is designed for high-performance model learning from different edges. A new individualized differential privacy approach with pixelization is proposed to protect users' privacy before sharing data. Simulation results show that FedRD achieves similar high detection performance (i.e., 90.32% accuracy) but with more than 1000 times wider coverage than the state-of-the-art, and works well when some edges only have limited samples; besides, it largely preserves users' privacy. Finally, despite the success of EcRD and FedRD in improving latency and protecting privacy, they are only based on a single modality (i.e., image/video) while nowadays, different modalities data becomes ubiquitous. Also, the communication cost of EcRD and FedRD are very high due to undifferentiated data transmission (both normal and dangerous data) and frequent model exchanges in its federated learning setting, respectively. A novel edge-cloud-based privacy-preserving Federated Multimodal learning framework for Road Danger detection and warning named FedMRD is introduced to leverage the multi-modality data in the real-world and reduce communication costs. In FedMRD, a novel multimodal road danger detection model considering both inter-and intra-class relations is developed. A communication-efficient federated learning strategy is proposed for collaborative model learning from edges with non-iid and imbalanced data. Further, a new multimodal differential privacy technique for high dimensional multimodal data with multiple attributes is introduced to protect data privacy directly on users' devices before uploading to edges. Experimental results demonstrate that FedMRD achieves around 96.42% higher accuracy with only 0.0351s latency and up to 250 times less communication cost compared with the state-of-the-art, and enables collaborative learning from multiple edges with non-iid and imbalanced data in different modalities while preservers users' privacy.2021-11-2

Make your own sprites: Aliasing-aware and cell-controllable pixelization

Pixel art is a unique art style with the appearance of low resolution images. In this paper, we propose a data-driven pixelization method that can pro- duce sharp and crisp cell effects with controllable cell sizes. Our approach overcomes the limitation of existing learning-based methods in cell size control by introducing a reference pixel art to explicitly regularize the cell structure. In particular, the cell structure features of the reference pixel art are used as an auxiliary input for the pixelization process, and for measuring the style similarity between the generated result and the reference pixel art. Furthermore, we disentangle the pixelization process into specific cell-aware and aliasing-aware stages, mitigating the ambiguities in joint learning of cell size, aliasing effect, and color assignment. To train our model, we construct a dedicated pixel art dataset and augment it with different cell sizes and different degrees of anti-aliasing effects. Extensive experiments demonstrate its superior performance over state-of-the-arts in terms of cell sharpness and perceptual expressiveness. We also show promising results of video game pixelization for the first time. Code and dataset are available at https://github.com/WuZongWei6/Pixelization

The Machine Learning Landscape of Top Taggers

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.Comment: Yet another tagger included