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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
6. On Effectiveness of the Adversarial Attacks on the Computer Systems of Biomedical Images Classification
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
11. Detect & Reject for Transferability of Black-Box Adversarial Attacks Against Network Intrusion Detection Systems
12. Trust-Based Adversarial Resiliency in Vehicular Cyber Physical Systems Using Reinforcement Learning
13. Deep Neural Network Based Malicious Network Activity Detection Under Adversarial Machine Learning Attacks
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
29. UnboundAttack: Generating Unbounded Adversarial Attacks to Graph Neural Networks
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
35. Unfooling SHAP and SAGE: Knockoff Imputation for Shapley Values
36. Reliable Aircraft Trajectory Prediction Using Autoencoder Secured with P2P Blockchain
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
40. Neutralizing Adversarial Machine Learning in Industrial Control Systems Using Blockchain
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
48. Defense Against Adversarial Attacks Using Chained Dual-GAN Approach
49. Consistency Regularization Helps Mitigate Robust Overfitting in Adversarial Training
50. Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives
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