# A Secure Fingerprinting Framework for Distributed Image Classification Source: [A Secure Fingerprinting Framework for Distributed Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:31:24.160Z Authors: Guowen Xu, Xingshuo Han, Anguo Zhang, Tianwei Zhang Published: 2022-07-11T07:21:24.000Z ## Abstract The deep learning (DL) technology has been widely used for image classification in many scenarios, e.g., face recognition and suspect tracking. Such a highly commercialized application has given rise to intellectual property protection of its DL model. To combat that, the mainstream method is to embed a unique watermark into the target model during the training process. However, existing efforts focus on detecting copyright infringement for a given model, while rarely consider the problem of traitors tracking. Moreover, the watermark embedding process can incur privacy issues for the training data in a distributed manner. In this paper, we propose SECUREMARK-DL, a novel fingerprinting framework to address the above two problems in a distributed learning environment. It embeds a unique fingerprint into the target model for each customer, which can be extracted and verified from any suspicious model once a dispute arises. In addition, it adopts a new privacy partitioning technique in the training process to protect the training data privacy. Extensive experiments demonstrate the robustness of SECUREMARK-DL against various attacks, and its high classification accuracy (\> 95%) even if a long-bit (304-bit) fingerprint is embedded into an input image. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()