ABSTRACT Background Object detection modules are essential functionalities for any autonomous vehicle. However, the performance of such modules implemented using deep neural networks can be unreliable in many cases, which raises the necessity to filter the abnormal outputs for safety considerations. Aims This article aims to develop a logical framework for filtering potentially erroneous object detection results. Materials & Methods Concretely, we consider two types of abstraction, namely spatial abstraction and temporal abstraction, based on the data labels from the training dataset of object detectors, and temporal consistency between a sequence of images. Operated on the training dataset, the construction of spatial abstraction iterates each input, aggregates region‐wise information over its associated labels, and stores the object abstraction. The abstraction is adopted to filter static and spatial abnormalities. The temporal abstraction builds an abstract transformer for a relaxed tracking algorithm. Elements being associated together by the abstract transformer can be checked against consistency over their original values. The abstraction helps to monitor temporal abnormality in consecutive frames. We have implemented the overall framework and validated it using publicly available datasets and open‐source object detectors. Results The implemented framework successfully identified most of static/spatial and temporal abnormalities in object detection outputs. Validation on public datasets confirmed the effectiveness of the abstraction‐based approach in filtering unreliable detections. Discussion The results demonstrate that logical abstractions derived from training data labels and temporal sequences provide a viable mechanism for monitoring the reliability of deep neural network‐based object detectors. Conclusion Abstraction‐based monitoring presents a robust and logical framework for enhancing the reliability of object detectors by filtering abnormal detection results.

In this paper, we introduce Def-VAE, a novel adversarial defense framework based on modeling real-world data distributions with Variational Autoencoders (VAEs), which can effectively defend image classifiers against adversarial attacks.Unlike traditional adversarial training methods that need to retrain the classifier, our approach does not rely on exposure to any adversarial examples during training, nor is it constrained to defend against specific models or attack algorithms.By leveraging the VAE's capability to learn the underlying distribution of clean data, we create a robust latent representation that can identify anomalous characteristics of adversarial inputs and figure out the original classifications.Experimental results demonstrate that Def-VAE achieves high defense success rates against diverse adversarial attacks for various datasets, showing the model and attack-agnostic resilience.

BACKGROUND: It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. In this study, an enhanced machine learning framework is established to diagnose the breast cancer. The core of this framework is to adopt fruit fly optimization algorithm (FOA) enhanced by Levy flight (LF) strategy (LFOA) to optimize two key parameters of support vector machine (SVM) and build LFOA-based SVM (LFOA-SVM) for diagnosing the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time. RESULTS: In order to verify the effectiveness of the proposed method, 10-fold cross-validation method is used to make comparison among the proposed method, FOA-SVM (model based on original FOA), PSO-SVM (model based on original particle swarm optimization), GA-SVM (model based on genetic algorithm), random forest, back propagation neural network and SVM. The main novelty of LFOA-SVM lies in the combination of FOA with LF strategy that enhances the quality for FOA, thus improving the convergence rate of the FOA optimization process as well as the probability of escaping from local optimal solution. CONCLUSIONS: The experimental results demonstrate that the proposed LFOA-SVM method can beat other counterparts in terms of various performance metrics. It can very well distinguish malignant breast cancer from benign ones and assist the doctor with clinical diagnosis.