  Adversarial attacks represent a substantial challenge in Natural Language
Processing (NLP). This study undertakes a systematic exploration of this
challenge in two distinct phases: vulnerability evaluation and resilience
enhancement of Transformer-based models under adversarial attacks.
  In the evaluation phase, we assess the susceptibility of three Transformer
configurations, encoder-decoder, encoder-only, and decoder-only setups, to
adversarial attacks of escalating complexity across datasets containing
offensive language and misinformation. Encoder-only models manifest a 14% and
21% performance drop in offensive language detection and misinformation
detection tasks, respectively. Decoder-only models register a 16% decrease in
both tasks, while encoder-decoder models exhibit a maximum performance drop of
14% and 26% in the respective tasks.
  The resilience-enhancement phase employs adversarial training, integrating
pre-camouflaged and dynamically altered data. This approach effectively reduces
the performance drop in encoder-only models to an average of 5% in offensive
language detection and 2% in misinformation detection tasks. Decoder-only
models, occasionally exceeding original performance, limit the performance drop
to 7% and 2% in the respective tasks. Although not surpassing the original
performance, Encoder-decoder models can reduce the drop to an average of 6% and
2% respectively.
  Results suggest a trade-off between performance and robustness, with some
models maintaining similar performance while gaining robustness. Our study and
adversarial training techniques have been incorporated into an open-source tool
for generating camouflaged datasets. However, methodology effectiveness depends
on the specific camouflage technique and data encountered, emphasizing the need
for continued exploration.
