Prototype-Guided Memory Replay for Continual Learning.

Continual learning (CL) is a machine learning paradigm that accumulates knowledge while learning sequentially. The main challenge in CL is catastrophic forgetting of previously seen tasks, which occurs due to shifts in the probability distribution. To retain knowledge, existing CL models often save some past examples and revisit them while learning new tasks. As a result, the size of saved samples dramatically increases as more samples are seen. To address this issue, we introduce an efficient CL method by storing only a few samples to achieve good performance. Specifically, we propose a dynamic prototype-guided memory replay (PMR) module, where synthetic prototypes serve as knowledge representations and guide the sample selection for memory replay. This module is integrated into an online meta-learning (OML) model for efficient knowledge transfer. We conduct extensive experiments on the CL benchmark text classification datasets and examine the effect of training set order on the performance of CL models. The experimental results demonstrate the superiority our approach in terms of accuracy and efficiency.

Frequency Spectrum Modification Process-Based Anti-Collusion Mechanism for Audio Signals.

The collusion attack combines multiple multimedia files into one new file to erase the user identity information. The traditional anti-collusion methods (which aim to trace the traitors) can defend the collusion attack, but they cannot well defend some hybrid collusion attacks (e.g., a collusion attack combined with desynchronization attacks). To address this issue, we propose a frequency spectrum modification process (FSMP) to defend the collusion attack by significantly downgrading the perceptual quality of the colluded file. The severe perceptual quality degradation can demotivate the attackers from launching the collusion attack. Because FSMP is orthogonal to the existing traitor-trace-based methods, it can be combined with the existing methods to provide a double-layer protection against different attacks. In FSMP, after several signal processing procedures (e.g., uneven framing and smoothing), multiple signals (called FSMP signals) can be generated from the host signal. Launching collusion attack using the generated FSMP signals would lead to the energy disturbance and attenuation effect (EDAE) over the colluded signals. Due to the EDAE, FSMP can significantly degrade the perceptual quality of the colluded audio file, thereby thwarting the collusion attack. In addition, FSMP can well defend different hybrid collusion attacks. Theoretical analysis and experimental results confirm the validity of the proposed method.