Trademark Logo Infringement Detection: A Threshold Determination Approach Using Bayesian Optimization and Siamese Neural Networks
Keywords:
Trademark, Bayesian Optimization, Siamese Neural Network, Triplet LossAbstract
Trademark infringement detection is essential to prevent consumer confusion, particularly in the digital era where visually similar logos are widespread. This study proposes a similarity threshold determination framework using Siamese Neural Networks (SNN) combined with Bayesian Optimization to improve the accuracy of trademark similarity assessment. Logo images were collected from the Indonesian intellectual property database, preprocessed into a uniform format, and trained using a triplet loss approach. Bayesian Optimization was applied to determine the optimal similarity threshold, minimizing false positive and false negative classifications. The proposed model achieved an accuracy of 92.23%, with precision of 93.34%, recall of 91.44%, and an F1-score of 92.39%. The optimal threshold (0.200149) effectively balanced sensitivity and specificity, resulting in low misclassification rates. These findings demonstrate that integrating SNN with Bayesian Optimization provides a robust and legally relevant framework for trademark infringement detection, offering practical implications for strengthening intellectual property protection.
References
[1] S. Cho and A. Moorman, "An examination of the psychometrical comparability of survey evidence in sport trademark litigation," J. Legal Aspects Sport, vol. 24, no. 1, pp. 3-23, 2014. doi: 10.1123/jlas.2013-0010.
[2] J. Kim, M. Ricci, and T. Serre, "Not-So-CLEVR: learning same–different relations strains feedforward neural networks," Interface Focus, vol. 8, no. 4, p. 20180011, 2018.
[3] B. Hanberry and H. He, "Prevalence, statistical thresholds, and accuracy assessment for species distribution models," Web Ecol., vol. 13, no. 1, pp. 13-19, 2013. doi: 10.5194/we-13-13-2013.
[4] S. Uchida, C. Shimada, N. Sakuma, F. Kagitani, A. Kan, and S. Awata, "Olfactory function and discrimination ability in the elderly: a pilot study," J. Physiological Sci., vol. 72, no. 1, 2022. doi: 10.1186/s12576-022-00832-6.
[5] F. Horen and R. Pieters, "When high-similarity copycats lose and moderate-similarity copycats gain: the impact of comparative evaluation," J. Mark. Res., vol. 49, no. 1, pp. 83-91, 2012. doi: 10.1509/jmr.08.0405.
[6] C. Colliander and P. Ahlgren, "Experimental comparison of first and second-order similarities in a scientometric context," Scientometrics, vol. 90, no. 2, pp. 675-685, 2011. doi: 10.1007/s11192-011-0491-x.
[7] F. Anuar, R. Setchi, and Y. Lai, "Semantic retrieval of trademarks based on conceptual similarity," IEEE Trans. Syst. Man Cybern. Syst., vol. 46, no. 2, pp. 220-233, 2016. doi: 10.1109/TSMC.2015.2421878.
[8] Q. H. Nguyen et al., "Influence of data splitting on performance of machine learning models in prediction of shear strength of soil," Math. Problems Eng., vol. 2021, no. 1, p. 4832864, 2021.
[9] M. Sivakumar, S. Parthasarathy, and T. Padmapriya, "Trade-off between training and testing ratio in machine learning for medical image processing," PeerJ Comput. Sci., vol. 10, e2245, 2024.
[10] A. H. Efat, S. M. Hasan, M. P. Uddin, and M. A. Mamun, "A multi-level ensemble approach for skin lesion classification using customized transfer learning with triple attention," PLoS One, vol. 19, no. 10, e0309430, 2024.
[11] D. Chicco, "Siamese neural networks: An overview," in Artificial Neural Networks, pp. 73-94, 2021.
[12] M. Shorfuzzaman and M. S. Hossain, "MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients," Pattern Recognit., vol. 113, p. 107700, 2021.
[13] K. Martin, N. Wiratunga, S. Sani, S. Massie, and J. Clos, "A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset," in CEUR Workshop Proceedings, June 2017.
[14] M. Zhang, Q. Cheng, F. Luo, and L. Ye, "A triplet nonlocal neural network with dual-anchor triplet loss for high-resolution remote sensing image retrieval," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 14, pp. 2711-2723, 2021.
[15] Y. Qiu, T. Misu, and C. Busso, "Use of triplet-loss function to improve driving anomaly detection using conditional generative adversarial network," in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1-7, Sep. 2020.
[16] Suyahman, Sunardi, Murinto, and Khusna, A. N. 2024. "Siamese Neural Networks with Chi-Square Distance for Trademark Image Similarity Detection." Scientific Journal of Informatics 11 (2): 429-438.
[17] P. I. Frazier, "A tutorial on Bayesian optimization," arXiv preprint arXiv:1807.02811, 2018.
[18] B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas, "Taking the human out of the loop: A review of Bayesian optimization," Proc. IEEE, vol. 104, no. 1, pp. 148-175, 2015.
[19] P. I. Frazier, "Bayesian optimization," in Recent Advances in Optimization and Modeling of Contemporary Problems, pp. 255-278, INFORMS, 2018.
[20] P. Feliot, J. Bect, and E. Vazquez, "A Bayesian approach to constrained single-and multi-objective optimization," J. Global Optim., vol. 67, no. 1, pp. 97-133, 2017.
[21] J. Berk, V. Nguyen, S. Gupta, S. Rana, and S. Venkatesh, "Exploration enhanced expected improvement for Bayesian optimization," in Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part II, vol. 18, pp. 621-637, Springer International Publishing, 2019.
[22] R. B. Gramacy, Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences. Chapman and Hall/CRC, 2020.
[23] B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas, "Taking the human out of the loop: A review of Bayesian optimization," Proc. IEEE, vol. 104, no. 1, pp. 148-175, 2015.
[24] G. Naidu, T. Zuva, and E. M. Sibanda, "A review of evaluation metrics in machine learning algorithms," in Computer Science On-line Conference, pp. 15-25, Cham: Springer International Publishing, Apr. 2023.
[25] M. Grandini, E. Bagli, and G. Visani, "Metrics for multi-class classification: an overview," arXiv preprint arXiv:2008.05756, 2020.
[26] R. Barandela, J. S. Sánchez, V. García, and E. Rangel, "Strategies for learning in class imbalance problems," Pattern Recognit., vol. 36, no. 3, pp. 849-851, 2003.
[27] A. Fisher, C. Rudin, and F. Dominici, "All models are wrong, but many are useful: Learning a variable's importance by studying an entire class of prediction models simultaneously," J. Mach. Learn. Res., vol. 20, no. 177, pp. 1-81, 2019.
[28] C. Ferri, J. Hernández-Orallo, and R. Modroiu, "An experimental comparison of performance measures for classification," Pattern Recognit. Lett., vol. 30, no. 1, pp. 27-38, 2009.
[29] S. K. Papia, M. A. Khan, T. Habib, M. Rahman, and M. N. Islam, "DistilRoBiLSTMFuse: an efficient hybrid deep learning approach for sentiment analysis," PeerJ Comput. Sci., vol. 10, e2349, 2024.
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