Shahrood University of Technology Journal of AI and Data Mining 2322-5211 Articles in Press 2026 06 06 A Deep Learning Approach for Authentication of Original and Non-Original Bank-Issued Gold Coins with Non-Uniform Directions in the Financial Market 3800 10.22044/jadm.2025.16177.2738 EN Mohammad M. AlyanNezhadi University of Science and Technology of Mazandaran Hesamoddin Pourrostami International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology Mousa Nazari Department of Computer Science, University of Science and Technology of Mazandaran, Behshahr, Mazandaran, Iran Farzan Afshari Department Of Physics, Semnan University, Semnan, Iran Journal Article 2025 05 05 In Iran’s financial market, the authentication of gold coins is majorly required for transparency, reducing fraud, and proper valuation. Differentiating between bank-issued and non-bank-issued coins pose a challenge as their appearance is almost the same. This paper suggests a classification method that is based on deep learning and has three main components: extracting area of interest, aligning images through a CNN regressor, and classifying coins through a CNN classifier. The method is tested on a set of 130 coins images (71 coins from banks and 59 coins from non-banks) and is benchmarked against baseline models employing feature extraction and SVMs. The proposed method outperforms the baseline with 99% accuracy. The results prove that the model works effectively in authenticating the coins, which enables safe transactions in the gold market.