Mind your indices! Index hijacking attacks on collaborative unpooling autoencoder systems

Autoencoder model architectures are attractive approaches for implementing intelligent mobile/IoT sensing applications. This is attributed to their capability of offering efficient codings of input data, which form the basis of efficient collaborative inference systems. In this work, we present a pair of novel attacks that threaten the security of collaborative unpooling-based autoencoder systems. We first demonstrate a reconstruction attack where the attacker exploits the autoencoder model's indices to reconstruct the original input (by hijacking the autoencoder's index transmissions between a local sensing platform and a remote server). We also demonstrate an adversarial attack where the attacker maliciously alters the index to output inaccurate inference results. The design of an effective input reconstruction model is a core component in successfully launching these index-based attacks and we show that practical deployment characteristics of mobile/IoT software allow such model design to be possible. Through comprehensive evaluations of three case study applications, we demonstrate the feasibility and effectiveness of the proposed index-based attacks and how they outperform conventional adversarial attack methods.