Adversarial Robustness of Phishing Email Detection: A Comparative Study of TF-IDF + Logistic Regression and Fine-Tuned DistilBERT

arXiv:2607.18429 · cs.CR, cs.AI, cs.CY, cs.LG · Submitted 2026-08-23 · Read on arXiv

Tanveer Ahmed, Seyedali Pourmoafil

cs.CR, cs.AI, cs.CY, cs.LG

Submitted: 2026-08-23

Updated: 2026-08-25

Code: https://github.com/ReDASers/IWSPA-2023-Adversarial-SyntheticDataset

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Robustness — 28× in this paper · explained in 5 episodes
• Accuracy — 27× in this paper · explained in 1 episode
• Adversarial Robustness — 20× in this paper · explained in 3 episodes
• Transformer — 11× in this paper · explained in 2 episodes
• Adversarial Examples — 4× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Explainability — 4× in this paper · explained in 3 episodes
• Anti-Phishing Working Group — 2× in this paper
• Failure Modes — 2× in this paper · explained in 3 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Explainable AI (XAI) — 1× in this paper · explained in 7 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Generating Natural Language Adversarial Examples
• DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

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