Learning Second Order Local Anomaly for General Face Forgery Detection

In this work, we propose a novel method to improve the generalization ability of CNN-based face forgery detectors. Our method considers the feature anomalies of forged faces caused by the prevalent blending operations in face forgery algorithms. Specifically, we propose a weakly supervised Second Order Local Anomaly (SOLA) learning module to mine anomalies in local regions using deep feature maps. SOLA first decomposes the neighborhood of local features by different directions and distances and then calculates the first and second order local anomaly maps which provide more general forgery traces for the classifier. We also propose a Local Enhancement Module (LEM) to improve the discrimination between local features of real and forged regions, so as to ensure accuracy in calculating anomalies. Besides, an improved Adaptive Spatial Rich Model (ASRM) is introduced to help mine subtle noise features via learnable high pass filters. With neither pixel level annotations nor external synthetic data, our method using a simple ResNet18 backbone achieves competitive performances compared with state-of-the-art works when evaluated on unseen forgeries

Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method

As the quality of optical sensors improves, there is a need for processing large-scale images. In particular, the ability of devices to capture ultra-high definition (UHD) images and video places new demands on the image processing pipeline. In this paper, we consider the task of low-light image enhancement (LLIE) and introduce a large-scale database consisting of images at 4K and 8K resolution. We conduct systematic benchmarking studies and provide a comparison of current LLIE algorithms. As a second contribution, we introduce LLFormer, a transformer-based low-light enhancement method. The core components of LLFormer are the axis-based multi-head self-attention and cross-layer attention fusion block, which significantly reduces the linear complexity. Extensive experiments on the new dataset and existing public datasets show that LLFormer outperforms state-of-the-art methods. We also show that employing existing LLIE methods trained on our benchmark as a pre-processing step significantly improves the performance of downstream tasks, e.g., face detection in low-light conditions. The source code and pre-trained models are available at https://github.com/TaoWangzj/LLFormer

Initial Experience with Hybrid Partial Nephrectomy with Ultrasound-guided Balloon Catheter Occlusion of the Renal Artery for Recurrent Renal Tumors

Repeat partial nephrectomy (PN) is an effective treatment in improving the prognosis for patients with recurrent renal cancer after initial PN. However, salvage PN (sPN) is inevitably associated with a higher rate of complications, largely because of intraperitoneal adhesions and fibrosis. Here we describe three initial cases for which recurrent renal tumors were treated with a novel minimally invasive approach, namely Ultrasound-guided Renal Artery Balloon catheter Occluded Hybrid Partial Nephrectomy (UBo-HPN).With laparoscopic ultrasound (LUS) guiding a Fogarty catheter to occlude the arterial blood supply, dissection of the renal hilum and most of the abdominal cavity can be avoided. UBo-HPN was successfully performed in three patients. One case of postoperative fever (Clavien-Dindo grade II) occurred, with no other complications. The mean operative time was 106 min, with a mean warm ischemia time of 21 min. UBo-HPN may be considered a safe and effective alternative for sPN, with a minimally invasive surgical footprint and better surgical outcomes