  The exponential adoption of machine learning (ML) is propelling the world
into a future of intelligent automation and data-driven solutions. However, the
proliferation of malicious data manipulation attacks against ML, namely
adversarial and backdoor attacks, jeopardizes its reliability in
safety-critical applications. The existing detection methods against such
attacks are built upon assumptions, limiting them in diverse practical
scenarios. Thus, motivated by the need for a more robust and unified defense
mechanism, we investigate the shared traits of adversarial and backdoor attacks
and propose NoiSec that leverages solely the noise, the foundational root cause
of such attacks, to detect any malicious data alterations. NoiSec is a
reconstruction-based detector that disentangles the noise from the test input,
extracts the underlying features from the noise, and leverages them to
recognize systematic malicious manipulation. Experimental evaluations conducted
on the CIFAR10 dataset demonstrate the efficacy of NoiSec, achieving AUROC
scores exceeding 0.954 and 0.852 under white-box and black-box adversarial
attacks, respectively, and 0.992 against backdoor attacks. Notably, NoiSec
maintains a high detection performance, keeping the false positive rate within
only 1\%. Comparative analyses against MagNet-based baselines reveal NoiSec's
superior performance across various attack scenarios.
