Invariant Features in Language Models: Geometric Characterization and Model Attribution

Under review

We propose a local geometric framework for identifying invariant semantic subspaces in transformer-based language models, with applications to zero-shot model attribution across diverse LLM architectures.

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TIACam: Text-Anchored Invariant Feature Learning with Auto-Augmentation for Camera-Robust Zero-Watermarking

IEEE/CVF Computer Vision and Pattern Recognition Conference (in press)

TIACam is a text-anchored invariant feature learning framework for camera-robust zero-watermarking that embeds messages in a distortion-invariant feature space.

Paper

Watermarking Language Models through Language Models

IEEE Transactions on Artificial Intelligence 2025

Prompt-based LLM watermarking framework that embeds detectable signals in model responses without modifying weights or data.

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Robust Image Watermarking via Cross-Attention & Invariant Domain Learning

International Conf. on Computational Science & Computational Intelligence 2023

Watermark embedding and extraction method resilient to geometric and photometric attacks using ViT-based cross-attention and invariant domain learning.

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Perspective Transformation Layer

International Conf. on Computational Science & Computational Intelligence 2022

A lightweight differentiable layer that learns perspective transformations within the network forward pass, improving robustness to viewpoint changes.

Paper · Code