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2. A Deep Dive into Deep Learning-Based Adversarial Attacks and Defenses in Computer Vision: From a Perspective of Cybersecurity

3. Making Domain Specific Adversarial Attacks for Retinal Fundus Images

4. An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection

5. On Real-Time Model Inversion Attacks Detection

7. Towards Improving the Anti-attack Capability of the RangeNet++

8. Transformers in Unsupervised Structure-from-Motion

9. Adversarial Attacks and Mitigations on Scene Segmentation of Autonomous Vehicles

10. Improving the Transferability of Adversarial Attacks Through Both Front and Rear Vector Method

14. Two to Trust: AutoML for Safe Modelling and Interpretable Deep Learning for Robustness

15. Pixel Based Adversarial Attacks on Convolutional Neural Network Models

16. Performance Evaluation of Adversarial Attacks on Whole-Graph Embedding Models

17. Towards Evaluating the Robustness of Deep Intrusion Detection Models in Adversarial Environment

18. Influence of Control Parameters and the Size of Biomedical Image Datasets on the Success of Adversarial Attacks

19. : Defending Against Adversarial Attacks Using Statistical Hypothesis Testing

20. Research on Neural Network Defense Problem Based on Random Noise Injection

21. Towards Explaining Shortcut Learning Through Attention Visualization and Adversarial Attacks

22. Evaluating Port Emissions Prediction Model Resilience Against Cyberthreats

23. The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking

24. Adversarial Attacks on Large Language Models

25. Different Attack and Defense Types for AI Cybersecurity

26. Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation

27. A Comparative Analysis of Evolutionary Adversarial One-Pixel Attacks

28. Adversarial Attacks and Defenses in Capsule Networks: A Critical Review of Robustness Challenges and Mitigation Strategies

30. Deceiving Airborne Object Detectors Using Adversarial AI

31. SCME: A Self-contrastive Method for Data-Free and Query-Limited Model Extraction Attack

32. Research on Transferable Characteristics of Adversarial Examples Generated Based on Gradient Information

33. Attack and Fault Injection in Self-driving Agents on the Carla Simulator – Experience Report

34. Rethinking the Evaluation of Deep Neural Network Robustness

37. Boosting Adversarial Transferability Through Intermediate Feature

38. Towards Robustness of Large Language Models on Text-to-SQL Task: An Adversarial and Cross-Domain Investigation

39. Data-Free Model Extraction Attacks in the Context of Object Detection

41. Backdoor Mitigation in Deep Neural Networks via Strategic Retraining

42. Preventing Adversarial Attacks on Autonomous Driving Models

43. Attribution-Based Confidence Metric for Detection of Adversarial Attacks on Breast Histopathological Images

44. A Security-Oriented Architecture for Federated Learning in Cloud Environments

45. Risk Susceptibility of Brain Tumor Classification to Adversarial Attacks

46. Are Graph Neural Network Explainers Robust to Graph Noises?

47. Adversarial Robustness of MR Image Reconstruction Under Realistic Perturbations

49. Consistency Regularization Helps Mitigate Robust Overfitting in Adversarial Training

50. Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives