  Machine learning models are vulnerable to maliciously crafted Adversarial
Examples (AEs). Training a machine learning model with AEs improves its
robustness and stability against adversarial attacks. It is essential to
develop models that produce high-quality AEs. Developing such models has been
much slower in natural language processing (NLP) than in areas such as computer
vision. This paper introduces a practical and efficient adversarial attack
model called SSCAE for \textbf{S}emantic, \textbf{S}yntactic, and
\textbf{C}ontext-aware natural language \textbf{AE}s generator. SSCAE
identifies important words and uses a masked language model to generate an
early set of substitutions. Next, two well-known language models are employed
to evaluate the initial set in terms of semantic and syntactic characteristics.
We introduce (1) a dynamic threshold to capture more efficient perturbations
and (2) a local greedy search to generate high-quality AEs. As a black-box
method, SSCAE generates humanly imperceptible and context-aware AEs that
preserve semantic consistency and the source language's syntactical and
grammatical requirements. The effectiveness and superiority of the proposed
SSCAE model are illustrated with fifteen comparative experiments and extensive
sensitivity analysis for parameter optimization. SSCAE outperforms the existing
models in all experiments while maintaining a higher semantic consistency with
a lower query number and a comparable perturbation rate.
