XMAM:X-raying Models with A Matrix to Reveal Backdoor Attacks for Federated Learning

Federated Learning (FL) has received increasing attention due to its privacy protection capability. However, the base algorithm FedAvg is vulnerable when it suffers from so-called backdoor attacks. Former researchers proposed several robust aggregation methods. Unfortunately, many of these aggregation methods are unable to defend against backdoor attacks. What's more, the attackers recently have proposed some hiding methods that further improve backdoor attacks' stealthiness, making all the existing robust aggregation methods fail. To tackle the threat of backdoor attacks, we propose a new aggregation method, X-raying Models with A Matrix (XMAM), to reveal the malicious local model updates submitted by the backdoor attackers. Since we observe that the output of the Softmax layer exhibits distinguishable patterns between malicious and benign updates, we focus on the Softmax layer's output in which the backdoor attackers are difficult to hide their malicious behavior. Specifically, like X-ray examinations, we investigate the local model updates by using a matrix as an input to get their Softmax layer's outputs. Then, we preclude updates whose outputs are abnormal by clustering. Without any training dataset in the server, the extensive evaluations show that our XMAM can effectively distinguish malicious local model updates from benign ones. For instance, when other methods fail to defend against the backdoor attacks at no more than 20% malicious clients, our method can tolerate 45% malicious clients in the black-box mode and about 30% in Projected Gradient Descent (PGD) mode. Besides, under adaptive attacks, the results demonstrate that XMAM can still complete the global model training task even when there are 40% malicious clients. Finally, we analyze our method's screening complexity, and the results show that XMAM is about 10-10000 times faster than the existing methods.Comment: 23 page

Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation

This paper aims to tackle the interactive behavior prediction task, and proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene-compliant trajectories of two interacting agents. Our CGTP framework is an end to end and interpretable model, including three main stages: context encoding, goal interactive prediction and trajectory interactive prediction. First, a Goals-of-Interest Network (GoINet) is designed to extract the interactive features between agent-to-agent and agent-to-goals using a graph-based vectorized representation. Further, the Conditional Goal Prediction Network (CGPNet) focuses on goal interactive prediction via a combined form of marginal and conditional goal predictors. Finally, the Goaloriented Trajectory Forecasting Network (GTFNet) is proposed to implement trajectory interactive prediction via the conditional goal-oriented predictors, with the predicted future states of the other interacting agent taken as inputs. In addition, a new goal interactive loss is developed to better learn the joint probability distribution over goal candidates between two interacting agents. In the end, the proposed method is conducted on Argoverse motion forecasting dataset, In-house cut-in dataset, and Waymo open motion dataset. The comparative results demonstrate the superior performance of our proposed CGTP model than the mainstream prediction methods.Comment: 14 pages, 4 figure

RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection

The widespread use of face retouching filters on short-video platforms has raised concerns about the authenticity of digital appearances and the impact of deceptive advertising. To address these issues, there is a pressing need to develop advanced face retouching techniques. However, the lack of large-scale and fine-grained face retouching datasets has been a major obstacle to progress in this field. In this paper, we introduce RetouchingFFHQ, a large-scale and fine-grained face retouching dataset that contains over half a million conditionally-retouched images. RetouchingFFHQ stands out from previous datasets due to its large scale, high quality, fine-grainedness, and customization. By including four typical types of face retouching operations and different retouching levels, we extend the binary face retouching detection into a fine-grained, multi-retouching type, and multi-retouching level estimation problem. Additionally, we propose a Multi-granularity Attention Module (MAM) as a plugin for CNN backbones for enhanced cross-scale representation learning. Extensive experiments using different baselines as well as our proposed method on RetouchingFFHQ show decent performance on face retouching detection. With the proposed new dataset, we believe there is great potential for future work to tackle the challenging problem of real-world fine-grained face retouching detection.Comment: Under revie

The global landscape of approved antibody therapies

Antibody therapies have become an important class of therapeutics in recent years as they have exhibited outstanding efficacy and safety in the treatment of several major diseases including cancers, immune-related diseases, infectious disease and hematological disease. There has been significant progress in the global research and development landscape of antibody therapies in the past decade. In this review, we have collected available data from the Umabs Antibody Therapies Database (Umabs-DB, https://umabs.com) as of 30 June 2022. The Umabs-DB shows that 162 antibody therapies have been approved by at least one regulatory agency in the world, including 122 approvals in the US, followed by 114 in Europe, 82 in Japan and 73 in China, whereas biosimilar, diagnostic and veterinary antibodies are not included in our statistics. Although the US and Europe have been at the leading position for decades, rapid advancement has been witnessed in Japan and China in the past decade. The approved antibody therapies include 115 canonical antibodies, 14 antibody-drug conjugates, 7 bispecific antibodies, 8 antibody fragments, 3 radiolabeled antibodies, 1 antibody-conjugate immunotoxin, 2 immunoconjugates and 12 Fc-Fusion proteins. They have been developed against 91 drug targets, of which PD-1 is the most popular, with 14 approved antibody-based blockades for cancer treatment in the world. This review outlined the global landscape of the approved antibody therapies with respect to the regulation agencies, therapeutic targets and indications, aiming to provide an insight into the trends of the global development of antibody therapies