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Found 5 papers, 1 papers with code
Self-Supervised Graph Transformer for Deepfake Detection
To assess the effectiveness of the proposed framework, several challenging experiments are conducted, including in-data distribution performance, cross-dataset, cross-manipulation generalization, and robustness against common post-production perturbations.
Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Prediction
The integration of multi-modal data, such as pathological images and genomic data, is essential for understanding cancer heterogeneity and complexity for personalized treatments, as well as for enhancing survival predictions.
Generating Adversarial Examples with an Optimized Quality
However, deep learning models are vulnerable to Adversarial Examples (AEs), carefully crafted samples to deceive those models.
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection
Therefore, we proposed an approach to generate adversarial examples, COPYCAT, which is specifically designed for malware detection systems considering two main goals; achieving a high misclassification rate and maintaining the executability and functionality of the original input.
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems
The main goal of this study is to investigate the robustness of graph-based Deep Learning (DL) models used for Internet of Things (IoT) malware classification against Adversarial Learning (AL).
