  The increasing popularity of deep learning (DL) models and the advantages of
computing, including low latency and bandwidth savings on smartphones, have led
to the emergence of intelligent mobile applications, also known as DL apps, in
recent years. However, this technological development has also given rise to
several security concerns, including adversarial examples, model stealing, and
data poisoning issues. Existing works on attacks and countermeasures for
on-device DL models have primarily focused on the models themselves. However,
scant attention has been paid to the impact of data processing disturbance on
the model inference. This knowledge disparity highlights the need for
additional research to fully comprehend and address security issues related to
data processing for on-device models. In this paper, we introduce a data
processing-based attacks against real-world DL apps. In particular, our attack
could influence the performance and latency of the model without affecting the
operation of a DL app. To demonstrate the effectiveness of our attack, we carry
out an empirical study on 517 real-world DL apps collected from Google Play.
Among 320 apps utilizing MLkit, we find that 81.56\% of them can be
successfully attacked.
  The results emphasize the importance of DL app developers being aware of and
taking actions to secure on-device models from the perspective of data
processing.
