Generalised Eigenvalue Geometry of Semantic Adversarial Attacks
Recent empirical work shows that semantically equivalent paraphrases can fool financial sentiment classifiers: although a paraphrase remains close to the original under a strong reference embedding, it may shift the target model's representation enough to change the predicted class. Existing robustness theory either assumes a single-model threat model or focuses mainly on empirical attack algorithms. We develop a continuous local model of semantic paraphrase perturbations that captures this two-model structure. We show that the worst-case local displacement of the target representation, subject to a proxy-model budget, is governed by the largest generalised eigenvalue of a matrix pencil $...
| Authors | Martin Anthony, Kaveh Salehzadeh Nobari |
|---|---|
| Primary category | stat.ML |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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