  The size of deep learning models in artificial intelligence (AI) software is
increasing rapidly, hindering the large-scale deployment on resource-restricted
devices (e.g., smartphones). To mitigate this issue, AI software compression
plays a crucial role, which aims to compress model size while keeping high
performance. However, the intrinsic defects in a big model may be inherited by
the compressed one. Such defects may be easily leveraged by adversaries, since
a compressed model is usually deployed in a large number of devices without
adequate protection. In this article, we aim to address the safe model
compression problem from the perspective of safety-performance co-optimization.
Specifically, inspired by the test-driven development (TDD) paradigm in
software engineering, we propose a test-driven sparse training framework called
SafeCompress. By simulating the attack mechanism as safety testing,
SafeCompress can automatically compress a big model to a small one following
the dynamic sparse training paradigm. Then, considering two kinds of
representative and heterogeneous attack mechanisms, i.e., black-box membership
inference attack and white-box membership inference attack, we develop two
concrete instances called BMIA-SafeCompress and WMIA-SafeCompress. Further, we
implement another instance called MMIA-SafeCompress by extending SafeCompress
to defend against the occasion when adversaries conduct black-box and white-box
membership inference attacks simultaneously. We conduct extensive experiments
on five datasets for both computer vision and natural language processing
tasks. The results show the effectiveness and generalizability of our
framework. We also discuss how to adapt SafeCompress to other attacks besides
membership inference attack, demonstrating the flexibility of SafeCompress.
