Federated learning (FL) has a great potential in large-scale machine learning applications by training a global model over distributed client data. However, FL deployed in real-world applications often incur collaboration bias and unstable …
Recent studies show that an ensemble of deep networks can have better adversarial robustness by increasing the learning diversity of base models to limit adversarial transferability. However, existing schemes mostly rely on a second-order method for …
Distilled student models in teacher-student architectures are widely considered for computational-effective deployment in real-time applications and edge devices. However, there is a higher risk of student models to encounter adversarial attacks at …
Existing adversarial face detectors are mostly developed against specific types of attacks, and limited by their generalizability especially in adversarial settings. In this paper, we propose a new detection strategy based on a dual-classifier driven …
Collaborative learning is used in multi-media applications to distribute computing tasks and data storage over multiple sites. Recent studies found that private data information can be derived from model updates between the server and clients. Yet, …
Out-of-distribution (OOD) detection is critical for safely deploying machine learning models in the open world. Recently, an energy-score based OOD detector was proposed for any pre-trained classification models. The energy score, which is less …
Deep hashing based retrieval models have been widely used in large-scale image retrieval systems. Recently, there has been a surging interest in studying the adversarial attack problem in deep hashing based retrieval models. However, the …
There is an increasing interest in studying adversarial attacks on image retrieval systems. However, most of the existing attack methods are based on the white-box setting, where the attackers have access to all the model and database details, which …
Learning-based classifiers are susceptible to adversarial examples. Existing defence methods are mostly devised on individual classifiers. Recent studies showed that it is viable to increase adversarial robustness by promoting diversity over an …
Entity resolution (ER) aims to identify entity records that refer to the same real-world entity, which is a critical problem in data cleaning and integration. Most of the existing models are attribute-centric, that is, matching entity pairs by …