The rapid proliferation of image generation models and other artificial intelligence (AI) systems has intensified concerns regarding data privacy and user consent. As the availability of public datasets declines, major technology companies increasingly rely on proprietary or private user data for model training, raising ethical and legal challenges when users request the deletion of their data after it has influenced a trained model. Machine unlearning seeks to address this issue by enabling the removal of specific data from models without complete retraining. This study investigates a modified SISA (Sharded, Isolated, Sliced, and Aggregated) framework designed to achieve class-level unlearning in Convolutional Neural Network (CNN) architectures. The proposed framework incorporates a reinforced replay mechanism and a gating network to enhance selective forgetting efficiency. Experimental evaluations across multiple image datasets and CNN configurations demonstrate that the modified SISA approach enables effective class unlearning while preserving model performance and reducing retraining overhead. The findings highlight the potential of SISA-based unlearning for deployment in privacy-sensitive AI applications. The implementation is publicly available at https://github.com/SiamFS/ sisa-class-unlearning.

翻译：图像生成模型及其他人工智能系统的快速发展加剧了数据隐私与用户授权的相关担忧。随着公开数据集可用性的下降，大型科技公司日益依赖专有或私有用户数据进行模型训练，当用户要求在已影响训练模型的数据被删除时，引发了道德与法律层面的挑战。机器遗忘技术旨在通过无需完全重训练即可从模型中移除特定数据来解决该问题。本研究探索了一种改进型SISA（分片、隔离、切片与聚合）框架，该框架专为卷积神经网络架构中的类别级遗忘设计。所提框架整合了增强型重放机制与门控网络，以提升选择性遗忘效率。在多个图像数据集与CNN配置上的实验评估表明，改进后的SISA方法能够有效实现类别遗忘，同时保持模型性能并降低重训练开销。研究结果凸显了基于SISA的遗忘技术在隐私敏感型AI应用中的部署潜力。相关实现代码已公开于https://github.com/SiamFS/sisa-class-unlearning。