Although effective deepfake detection models have been developed in recent years recent
studies have revealed that these models can result in unfair performance disparities among …

Generative adversarial network (GAN) generated high-realistic human faces are visually
challenging to discern from real ones. They have been used as profile images for fake social …

Face manipulation techniques, especially DeepFake techniques, are causing severe social
concerns and security problems. When faced with skewed data distributions such as those …

Despite the development of effective deepfake detectors in recent years, recent studies have
demonstrated that biases in the data used to train these detectors can lead to disparities in …

Generative Adversarial Network (GAN) based techniques can generate and synthesize
realistic faces that cause profound social concerns and security problems. Existing methods …

Top-k multi-label learning, which returns the top-k predicted labels from an input, has many
practical applications such as image annotation, document analysis, and web search …

Recent works have revealed an essential paradigm in designing loss functions that
differentiate individual losses versus aggregate losses. The individual loss measures the …

In the realm of medical imaging, particularly for COVID-19 detection, deep learning models
face substantial challenges such as the necessity for extensive computational resources, the …

We propose a general approach for training survival analysis models that minimizes a worst-
case error across all subpopulations that are large enough (occurring with at least a user …

Biomedical image segmentation is critical for accurate identification and analysis of
anatomical structures in medical imaging, particularly in cardiac MRI. Manual segmentation …