The quality and accessibility of modern financial service have been quickly and dramatically improved, which benefits from the fast development of information technology. It has also witnessed the trend for applying artificial intelligence related technology, especially machine learning to the finance and security industry ranging from face recognition to fraud detection. In particular, deep neural networks have proven to be far superior to traditional algorithms in various application scenarios of computer vision. In this paper, we propose a deep learning-based video analysis system for automated compliance audit in stock brokerage, which in general consists of five modules here: 1) Video tampering and integrity detection; 2) Objects of interest localization and association; 3) Analysis of presence and departure of personnel in a video; 4) Face image quality assessment; and 5) Signature action positioning. To the best of our knowledge, this is the first work that introduces remote automated compliance audit system for the dual-recorded video in finance and security industry. The experimental results suggest our system can identify most of the potential non-compliant videos and has greatly improved the working efficiency of the auditors and reduced human labor costs. The collected dataset in our experiment will be released with this paper.
Deep learning method have been offering promising solutions for medical image processing, but failing to understand what features in the input image are captured and whether certain artifacts are mistakenly included in the model, thus create crucial problems in generalizability of the model. We targeted a common issue of this kind caused by manual annotations appeared in medical image. These annotations are usually made by the doctors at the spot of medical interest and have adversarial effect on many computer vision AI tasks. We developed an inpainting algorithm to remove the annotations and recover the original images. Besides we applied variational information bottleneck method in order to filter out the unwanted features and enhance the robustness of the model. Our impaiting algorithm is extensively tested in object detection in thyroid ultrasound image data. The mAP (mean average precision, with IoU = 0.3) is 27% without the annotation removal. The mAP is 83% if manually removed the annotations using Photoshop and is enhanced to 90% using our inpainting algorithm. Our work can be utilized in future development and evaluation of artificial intelligence models based on medical images with defects.
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