Huge amounts of unstructured data including image, video, audio, and text are ubiquitously
generated and shared, and it is a challenge to protect sensitive personal information in …

The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …

Image inpainting has been widely exploited in the field of computer vision and image
processing. The main purpose of image inpainting is to produce visually plausible structure …

The free access to large-scale public databases, together with the fast progress of deep
learning techniques, in particular Generative Adversarial Networks, have led to the …

AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging
problem threatening the trustworthiness of online information. The need to develop and …

Meta-learning has been proposed as a framework to address the challenging few-shot
learning setting. The key idea is to leverage a large number of similar few-shot tasks in order …

Abstract Machine Learning (ML) models are increasingly deployed in the wild to perform a
wide range of tasks. In this work, we ask to what extent can an adversary steal functionality …

Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective for privacy
protection; nevertheless, object recognition research typically assumes access to complete …

Today's proliferation of powerful facial recognition systems poses a real threat to personal
privacy. As Clearview. ai demonstrated, anyone can canvas the Internet for data and train …

Generating novel, yet realistic, images of persons is a challenging task due to the complex
interplay between the different image factors, such as the foreground, background and pose …