Mitigating Backdoor Attacks using Activation-Guided Model Editing

Backdoor attacks compromise the integrity and reliability of machine learning models by embedding a hidden trigger during the training process, which can later be activated to cause unintended misbehavior. We propose a novel backdoor mitigation approach via machine unlearning to counter such backdoor attacks. The proposed method utilizes model activation of domain-equivalent unseen data to guide the editing of the model's weights. Unlike the previous unlearning-based mitigation methods, ours is computationally inexpensive and achieves state-of-the-art performance while only requiring a handful of unseen samples for unlearning. In addition, we also point out that unlearning the backdoor may cause the whole targeted class to be unlearned, thus introducing an additional repair step to preserve the model's utility after editing the model. Experiment results show that the proposed method is effective in unlearning the backdoor on different datasets and trigger patterns.

Shallow learning enables real-time inference of molecular composition changes from broadband-near-infrared spectroscopy of brain tissue

Spectroscopy-based imaging modalities such as near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) represent a promising alternative for low-cost, non-invasive, and fast monitoring of functional and structural properties of living tissue. Particularly, the possibility of extracting the molecular composition of the tissue from the optical spectra in real-time deems the spectroscopy techniques as unique diagnostic tools. However, due to the highly limited availability of paired optical and molecular profiling studies, building a mapping between a spectral signature and a corresponding set of molecular concentrations is still an unsolved problem. Moreover, there are no yet established methods to streamline inference of the biochemical composition from the optical spectrum for real-time applications such as surgical monitoring. In this paper, we develop a technique for fast inference of changes in the molecular composition of brain tissue. We base our method on the Beer-Lambert law to analytically connect the spectra with concentrations and use a deep-learning approach to significantly speed up the concentration inference compared to traditional optimization methods. We test our approach on real data obtained from the broadband NIRS study of piglets' brains. The results demonstrate that the proposed method enables real-time molecular composition inference while maintaining the accuracy of traditional optimization.