The success of machine learning (ML) techniques in the formerly difficult areas of data
analysis and pattern extraction has led to their widespread incorporation into various …

Differential privacy is the de-facto technique for protecting the individuals in the training
dataset and the learning models in deep learning. However, the technique presents two …

The exponential increase of Android malware creates a severe threat, motivating the
development of machine learning and especially deep learning-based classifiers to detect …

Data Poisoning Attacks (DPA) represent a sophisticated technique aimed at distorting the
training data of machine learning models, thereby manipulating their behavior. This process …

Machine learning (ML) models cannot neglect risks to security, privacy, and fairness.
Several defenses have been proposed to mitigate such risks. When a defense is effective in …

Model accuracy is the traditional metric employed in machine learning (ML) applications.
However, privacy, fairness, and robustness guarantees are crucial as ML algorithms …

The analog neural network to spiking neural network (ANN-to-SNN) conversion is an
effective method for improving the performance of SNNs. However, the existing mainstream …

Machine learning models have shone in a variety of domains and attracted increasing
attention from both the security and the privacy communities. One important yet worrying …

Machine learning models are susceptible to a variety of attacks that can erode trust in their
deployment. These threats include attacks against the privacy of training data and …

Deep neural networks (DNNs) have achieved great success in various computer vision
tasks. However, they are susceptible to artificially designed adversarial perturbations, which …