Voting-based leaderboards such as LM Arena are central to open-ended LLM evaluation, but their reliability depends on response anonymity. Prior work shows that surface features such as bag-of-words and TF-IDF can reveal model identity from the response, yet such signals are unreliable when models are stylistically similar or closely matched, and are easily down-weighted during vote aggregation. To examine the remaining anonymity risk, we introduce INTERPOL, a model-driven identification framework that learns target-specific response signatures from interpolated preference data. INTERPOL synthesizes controllable hard negatives by interpolating a target-mimicking copy model with its initial backbone, and trains a target-specific detector via curriculum-based triplet preference learning. Across three frontier target LLMs and two query sources, INTERPOL yields average relative gains of 32% in Accuracy and 25% in AUROC over prior methods. Moreover, in arena-style promotion simulations, INTERPOL attains the same ranking gain with about 19% fewer interactions than existing identification baselines; with competitor-aware voting, interactions are further reduced by about 69.9% relative to target-only voting.