  Approximate Computing (AC) has emerged as a promising technique for achieving
energy-efficient architectures and is expected to become an effective technique
for reducing the electricity cost for cloud service providers (CSP). However,
the potential misuse of AC has not received adequate attention, which is a
coming crisis behind the blueprint of AC. Driven by the pursuit of illegal
financial profits, untrusted CSPs may deploy low-cost AC devices and deceive
clients by presenting AC services as promised accurate computing products,
while falsely claiming AC outputs as accurate results. This misuse of AC will
cause both financial loss and computing degradation to cloud clients. In this
paper, we define this malicious attack as DisHonest Approximate Computing
(DHAC) and analyze the technical challenges faced by clients in detecting such
attacks. To address this issue, we propose two golden model free detection
methods: Residual Class Check (RCC) and Forward-Backward Check (FBC). RCC
provides clients a low-cost approach to infer the residual class to which a
legitimate accurate output should belong. By comparing the residual class of
the returned result, clients can determine whether a computing service contains
any AC elements. FBC detects potential DHAC by computing an invertible check
branch using the intermediate values of the program. It compares the values
before entering and after returning from the check branch to identify any
discrepancies. Both RCC and FBC can be executed concurrently with real
computing tasks, enabling real-time DHAC detection with current inputs. Our
experimental results show that both RCC and FBC can detect over 96%-99% of DHAC
cases without misjudging any legitimate accurate results.
