  Recent advances in natural language processing (NLP) have led to the
development of large language models (LLMs) such as ChatGPT. This paper
proposes a methodology for developing and evaluating ChatGPT detectors for
French text, with a focus on investigating their robustness on out-of-domain
data and against common attack schemes. The proposed method involves
translating an English dataset into French and training a classifier on the
translated data. Results show that the detectors can effectively detect
ChatGPT-generated text, with a degree of robustness against basic attack
techniques in in-domain settings. However, vulnerabilities are evident in
out-of-domain contexts, highlighting the challenge of detecting adversarial
text. The study emphasizes caution when applying in-domain testing results to a
wider variety of content. We provide our translated datasets and models as
open-source resources. https://gitlab.inria.fr/wantoun/robust-chatgpt-detection
