The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency Response Team, Institute for Artificial Intelligence, Tsinghua University, and RealAI as part of the Zhongguancun International Frontier Technology Innovation Competition (https://www.zgc-aisc.com/en). The competition consists of three tracks, including Deepfake Security Competition, Autonomous Driving Security Competition, and Face Recognition Security Competition. This report will introduce the competition rules of these three tracks and the solutions of top-ranking teams in each track.

Object goal navigation (ObjectNav) in unseen environments is a fundamental task for Embodied AI. Agents in existing works learn ObjectNav policies based on 2D maps, scene graphs, or image sequences. Considering this task happens in 3D space, a 3D-aware agent can advance its ObjectNav capability via learning from fine-grained spatial information. However, leveraging 3D scene representation can be prohibitively unpractical for policy learning in this floor-level task, due to low sample efficiency and expensive computational cost. In this work, we propose a framework for the challenging 3D-aware ObjectNav based on two straightforward sub-policies. The two sub-polices, namely corner-guided exploration policy and category-aware identification policy, simultaneously perform by utilizing online fused 3D points as observation. Through extensive experiments, we show that this framework can dramatically improve the performance in ObjectNav through learning from 3D scene representation. Our framework achieves the best performance among all modular-based methods on the Matterport3D and Gibson datasets, while requiring (up to 30x) less computational cost for training.

多目标多摄像机跟踪（MTMCT）在智能视频分析，监视视频检索和其他应用程序方案中起着重要作用。如今，基于深度学习的MTMCT一直是主流，并且在跟踪准确性和效率方面取得了令人着迷的改进。但是，根据我们的调查，缺乏关注现实应用程序方案的数据集限制了当前基于学习的MTMCT模型的进一步改进。具体而言，基于学习的MTMCT模型通过通用数据集培训通常无法在现实世界应用方案中获得令人满意的结果。在此激励的情况下，本文提出了一个半自动数据注释系统，以促进现实世界中的MTMCT数据集建立。拟议的系统首先采用基于深度学习的单相机轨迹生成方法来自动从监视视频中提取轨迹。随后，该系统在以下手动跨摄像机轨迹匹配过程中提供了建议列表。推荐列表是根据侧面信息生成的，包括相机位置，时间戳关系和背景场景。在实验阶段，广泛的结果进一步证明了拟议系统的效率。

COX比例危害模型是用于预测给定临床或遗传协变量患者的预期寿命的生存分析的规范方法 - 它是其原始形式的线性模型。近年来，已经提出了几种将COX模型推广到神经网络的方法，但是这些方法在数字上都不是正确的，并且在计算上都没有。我们提出了FastCPH，这是一种以线性时间运行的新方法，并支持绑扎事件的标准Breslow和EFRON方法。我们还证明了FastCPH与Lassonet的性能，Lassonet是一种神经网络，可通过特征稀疏性（生存数据集）提供解释性。最终过程是有效的，选择有用的协变量，并优于现有的Coxph方法。

人类具有以零拍的方式识别和获取新颖的视觉概念的非凡能力。考虑到以前学到的视觉概念及其关系的高级，象征性的描述，人类可以识别新颖的概念而不看到任何例子。此外，他们可以通过学习视觉概念和关系来解析和传达符号结构来获取新概念。赋予机器中的这些功能在提高推理时提高其概括能力方面至关重要。在这项工作中，我们介绍了零拍的概念识别和获取（ZEROC），这是一种神经符号结构，可以以零拍的方式识别和获取新颖的概念。 ZEROC代表概念作为组成概念模型的图（作为节点）及其关系（作为边缘）。为了允许推理时间组成，我们采用基于能量的模型（EBM）来建模概念和关系。我们设计ZEROC架构，以便它允许在概念的符号图结构及其相应的EBM之间进行一对一的映射，该图是第一次允许获取新概念，传达其图形结构并将其应用于分类和分类和在推理时检测任务（甚至跨域）。我们介绍了用于学习和推断ZEROC的算法。我们在一个充满挑战的网格世界数据集上评估了零，该数据集旨在探测零拍的概念识别和获取，并展示其功能。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.