IMPOSITION: Implicit Backdoor Attack through Scenario Injection

This paper presents a novel backdoor attack called IMPlicit BackdOor Attack through Scenario InjecTION (IMPOSITION) that does not require direct poisoning of the training data. Instead, the attack leverages a realistic scenario from the training data as a trigger to manipulate the model's output during inference. This type of attack is particularly dangerous as it is stealthy and difficult to detect. The paper focuses on the application of this attack in the context of Autonomous Driving (AD) systems, specifically targeting the trajectory prediction module. To implement the attack, we design a trigger mechanism that mimics a set of cloned behaviors in the driving scene, resulting in a scenario that triggers the attack. The experimental results demonstrate that IMPOSITION is effective in attacking trajectory prediction models while maintaining high performance in untargeted scenarios. Our proposed method highlights the growing importance of research on the trustworthiness of Deep Neural Network (DNN) models, particularly in safety-critical applications. Backdoor attacks pose a significant threat to the safety and reliability of DNN models, and this paper presents a new perspective on backdooring DNNs. The proposed IMPOSITION paradigm and the demonstration of its severity in the context of AD systems are significant contributions of this paper. We highlight the impact of the proposed attacks via empirical studies showing how IMPOSITION can easily compromise the safety of AD systems

CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous Driving

Benchmarking is a common method for evaluating trajectory prediction models for autonomous driving. Existing benchmarks rely on datasets, which are biased towards more common scenarios, such as cruising, and distance-based metrics that are computed by averaging over all scenarios. Following such a regiment provides a little insight into the properties of the models both in terms of how well they can handle different scenarios and how admissible and diverse their outputs are. There exist a number of complementary metrics designed to measure the admissibility and diversity of trajectories, however, they suffer from biases, such as length of trajectories. In this paper, we propose a new benChmarking paRadIgm for evaluaTing trajEctoRy predIction Approaches (CRITERIA). Particularly, we propose 1) a method for extracting driving scenarios at varying levels of specificity according to the structure of the roads, models' performance, and data properties for fine-grained ranking of prediction models; 2) A set of new bias-free metrics for measuring diversity, by incorporating the characteristics of a given scenario, and admissibility, by considering the structure of roads and kinematic compliancy, motivated by real-world driving constraints. 3) Using the proposed benchmark, we conduct extensive experimentation on a representative set of the prediction models using the large scale Argoverse dataset. We show that the proposed benchmark can produce a more accurate ranking of the models and serve as a means of characterizing their behavior. We further present ablation studies to highlight contributions of different elements that are used to compute the proposed metrics

Stacked Cross-modal Feature Consolidation Attention Networks for Image Captioning

Recently, the attention-enriched encoder-decoder framework has aroused great interest in image captioning due to its overwhelming progress. Many visual attention models directly leverage meaningful regions to generate image descriptions. However, seeking a direct transition from visual space to text is not enough to generate fine-grained captions. This paper exploits a feature-compounding approach to bring together high-level semantic concepts and visual information regarding the contextual environment fully end-to-end. Thus, we propose a stacked cross-modal feature consolidation (SCFC) attention network for image captioning in which we simultaneously consolidate cross-modal features through a novel compounding function in a multi-step reasoning fashion. Besides, we jointly employ spatial information and context-aware attributes (CAA) as the principal components in our proposed compounding function, where our CAA provides a concise context-sensitive semantic representation. To make better use of consolidated features potential, we further propose an SCFC-LSTM as the caption generator, which can leverage discriminative semantic information through the caption generation process. The experimental results indicate that our proposed SCFC can outperform various state-of-the-art image captioning benchmarks in terms of popular metrics on the MSCOCO and Flickr30K datasets

Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks

The purpose of this work was to assess the feasibility of acquisition time reduction in MPI-SPECT imaging using deep leering techniques through two main approaches, namely reduction of the acquisition time per projection and reduction of the number of angular projections