Toward Detecting Hidden Functionalities in Deep Learning Models

Deep functionality hiding is an emerging technique that embeds confidential or sensitive functions within seemingly benign deep learning models (DLMs), which perform ordinary machine learning tasks. This enables such models to execute covert tasks while remaining undetected. Despite the rapid progress in deep functionality hiding, countermeasures remain unexplored. In this paper, we propose Distribution Offset Analysis (DOA), a novel method for detecting hidden functionalities in DLMs. Our key insight is that the weight distribution of a benign DLM typically follows a Gaussian distribution, whereas a container DLM with hidden functionalities exhibits notable statistical deviations from this Gaussian pattern. In our methodology, we first compute the distributional distance (i.e.,offsets) between the model's weights and an ideal Gaussian distribution. We then fuse these offsets with weight features into a unified representation, which is subsequently used to train a meta-classifier for hidden functionality detection. Through extensive experiments, we demonstrate the effectiveness of the proposed DOA method, which achieves an average detection rate of over 87% against existing state-of-the-art deep functionality hiding techniques.