Deep learning-based monocular depth estimation (MDE) extensively applied in autonomous
driving is known to be vulnerable to adversarial attacks. Previous physical attacks against …

Deepfake represented by face swap** and face reenactment can transfer the appearance
and behavioral expressions of a face in one video image to another face in a different video …

SoK Neural Network Extraction-USENIX Presentation Page 1 SoK: Neural Network Extraction
Through Physical Side Channels 15.08.2024 Péter Horváth, Dirk Lauret, Zhuoran Liu, and …

State-of-the-art source code classification models exhibit excellent task transferability, in
which the source code encoders are first pre-trained on a source domain dataset in a self …

In recent years, deep neural networks (DNNs) have been widely applied across various
fields, but the sensitivity of DNNs to adversarial attacks has attracted widespread attention …

Transferable adversarial examples cause practical security risks since they can mislead a
target model without knowing its internal knowledge. A conventional recipe for maximizing …

Convolutional Neural Networks (CNNs), have demonstrated remarkable performance
across a range of domains, including computer vision and healthcare. However, they …

Despite remarkable achievements in deep learning across various domains, its inherent
vulnerability to adversarial examples still remains a critical concern for practical deployment …

Quantized neural networks (QNNs) are increasingly used for efficient deployment of deep
learning models on resource-constrained platforms, such as mobile devices and edge …

Convolutional Neural Networks (CNNs) have achieved superhuman performance in
computer vision tasks. However, these networks are becoming both increasingly complex …