Benchmarking Transferable Adversarial Attacks

The robustness of deep learning models against adversarial attacks remains a pivotal concern. This study presents, for the first time, an exhaustive review of the transferability aspect of adversarial attacks. It systematically categorizes and critically evaluates various methodologies developed to augment the transferability of adversarial attacks. This study encompasses a spectrum of techniques, including Generative Structure, Semantic Similarity, Gradient Editing, Target Modification, and Ensemble Approach. Concurrently, this paper introduces a benchmark framework \textit{TAA-Bench}, integrating ten leading methodologies for adversarial attack transferability, thereby providing a standardized and systematic platform for comparative analysis across diverse model architectures. Through comprehensive scrutiny, we delineate the efficacy and constraints of each method, shedding light on their underlying operational principles and practical utility. This review endeavors to be a quintessential resource for both scholars and practitioners in the field, charting the complex terrain of adversarial transferability and setting a foundation for future explorations in this vital sector. The associated codebase is accessible at: https://github.com/KxPlaug/TAA-BenchComment: Accepted by NDSS 2024 Worksho

GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation

The inherent ambiguity in ground-truth annotations of 3D bounding boxes caused by occlusions, signal missing, or manual annotation errors can confuse deep 3D object detectors during training, thus deteriorating the detection accuracy. However, existing methods overlook such issues to some extent and treat the labels as deterministic. In this paper, we formulate the label uncertainty problem as the diversity of potentially plausible bounding boxes of objects, then propose GLENet, a generative framework adapted from conditional variational autoencoders, to model the one-to-many relationship between a typical 3D object and its potential ground-truth bounding boxes with latent variables. The label uncertainty generated by GLENet is a plug-and-play module and can be conveniently integrated into existing deep 3D detectors to build probabilistic detectors and supervise the learning of the localization uncertainty. Besides, we propose an uncertainty-aware quality estimator architecture in probabilistic detectors to guide the training of IoU-branch with predicted localization uncertainty. We incorporate the proposed methods into various popular base 3D detectors and demonstrate significant and consistent performance gains on both KITTI and Waymo benchmark datasets. Especially, the proposed GLENet-VR outperforms all published LiDAR-based approaches by a large margin and ranks $1^{st}$ among single-modal methods on the challenging KITTI test set. We will make the source code and pre-trained models publicly available

SST: A Simplified Swin Transformer-based Model for Taxi Destination Prediction based on Existing Trajectory

Accurately predicting the destination of taxi trajectories can have various benefits for intelligent location-based services. One potential method to accomplish this prediction is by converting the taxi trajectory into a two-dimensional grid and using computer vision techniques. While the Swin Transformer is an innovative computer vision architecture with demonstrated success in vision downstream tasks, it is not commonly used to solve real-world trajectory problems. In this paper, we propose a simplified Swin Transformer (SST) structure that does not use the shifted window idea in the traditional Swin Transformer, as trajectory data is consecutive in nature. Our comprehensive experiments, based on real trajectory data, demonstrate that SST can achieve higher accuracy compared to state-of-the-art methods.Comment: Accepted by IEEE ITS

SAMMate: a GUI tool for processing short read alignments in SAM/BAM format

<p>Abstract</p> <p>Background</p> <p>Next Generation Sequencing (NGS) technology generates tens of millions of short reads for each DNA/RNA sample. A key step in NGS data analysis is the short read alignment of the generated sequences to a reference genome. Although storing alignment information in the Sequence Alignment/Map (SAM) or Binary SAM (BAM) format is now standard, biomedical researchers still have difficulty accessing this information.</p> <p>Results</p> <p>We have developed a Graphical User Interface (GUI) software tool named SAMMate. SAMMate allows biomedical researchers to quickly process SAM/BAM files and is compatible with both single-end and paired-end sequencing technologies. SAMMate also automates some standard procedures in DNA-seq and RNA-seq data analysis. Using either standard or customized annotation files, SAMMate allows users to accurately calculate the short read coverage of genomic intervals. In particular, for RNA-seq data SAMMate can accurately calculate the gene expression abundance scores for customized genomic intervals using short reads originating from both exons and exon-exon junctions. Furthermore, SAMMate can quickly calculate a whole-genome signal map at base-wise resolution allowing researchers to solve an array of bioinformatics problems. Finally, SAMMate can export both a wiggle file for alignment visualization in the UCSC genome browser and an alignment statistics report. The biological impact of these features is demonstrated via several case studies that predict miRNA targets using short read alignment information files.</p> <p>Conclusions</p> <p>With just a few mouse clicks, SAMMate will provide biomedical researchers easy access to important alignment information stored in SAM/BAM files. Our software is constantly updated and will greatly facilitate the downstream analysis of NGS data. Both the source code and the GUI executable are freely available under the GNU General Public License at <url>http://sammate.sourceforge.net</url>.</p

Positioning Using Visible Light Communications: A Perspective Arcs Approach

Visible light positioning (VLP) is an accurate indoor positioning technology that uses luminaires as transmitters. In particular, circular luminaires are a common source type for VLP, that are typically treated only as point sources for positioning, while ignoring their geometry characteristics. In this paper, the arc feature of the circular luminaire and the coordinate information obtained via visible light communication (VLC) are jointly used for VLC-enabled indoor positioning, and a novel perspective arcs approach is proposed. The proposed approach does not rely on any inertial measurement unit, and has no tilted angle limitations at the user. First, a VLC assisted perspective circle and arc algorithm (V-PCA) is proposed for a scenario in which a complete luminaire and an incomplete one can be captured by the user. Considering the cases in which parts of VLC links are blocked, an anti-occlusion VLC assisted perspective arcs algorithm (OA-V-PA) is proposed. Simulation results show that the proposed indoor positioning algorithm can achieve a 95th percentile positioning accuracy of around 10 cm. Moreover, an experimental prototype based on mobile phone is implemented, in which, a fused image processing method is proposed. Experimental results show that the average positioning accuracy is less than 5 cm

DANAA: Towards transferable attacks with double adversarial neuron attribution

While deep neural networks have excellent results in many fields, they are susceptible to interference from attacking samples resulting in erroneous judgments. Feature-level attacks are one of the effective attack types, which targets the learnt features in the hidden layers to improve its transferability across different models. Yet it is observed that the transferability has been largely impacted by the neuron importance estimation results. In this paper, a double adversarial neuron attribution attack method, termed `DANAA', is proposed to obtain more accurate feature importance estimation. In our method, the model outputs are attributed to the middle layer based on an adversarial non-linear path. The goal is to measure the weight of individual neurons and retain the features that are more important towards transferability. We have conducted extensive experiments on the benchmark datasets to demonstrate the state-of-the-art performance of our method. Our code is available at: https://github.com/Davidjinzb/DANAAComment: Accepted by 19th International Conference on Advanced Data Mining and Applications. (ADMA 2023