Data poisoning attacks (DPAs) are becoming popular as artificial intelligence (AI) algorithms, machine learning (ML) algorithms, and deep learning (DL) algorithms in this artificial intelligence (AI) era. Hackers and penetration testers are excessively injecting malicious contents in the training data (and in testing data too) that leads to false results that are very hard to inspect and predict. We have analyzed several recent technologies used (from deep reinforcement learning to federated learning) for the DPAs and their safety, security, & countermeasures. The problem setup along with the problem estimation is shown in the MuJoCo environment with performance of HalfCheetah before the dataset is poisoned and after the dataset is poisoned. We have analyzed several risks associated with the DPAs and falsification in medical data from popular poisoning data attacks to some popular data defenses. We have proposed robust offline reinforcement learning (Offline RL) for the safety and reliability with weighted hash verification along with density-ratio weighted behavioral cloning (DWBC) algorithm. The four stages of the proposed algorithm (as the Stage 0, the Stage 1, the Stage 2, and the Stage 3) are described with respect to offline RL, safety, and security for DPAs. The conclusion and future scope are provided with the intent to combine DWBC with other data defense strategies to counter and protect future contamination cyberattacks.

翻译：在当今人工智能时代，随着人工智能算法、机器学习算法及深度学习算法的广泛应用，数据投毒攻击正日益盛行。黑客与渗透测试者通过在训练数据（及测试数据）中大量注入恶意内容，导致产生难以检测和预测的错误结果。本文系统分析了从深度强化学习到联邦学习等多种近期用于应对数据投毒攻击的技术及其安全性、防护机制与应对策略。我们在MuJoCo环境中展示了问题构建与评估过程，通过HalfCheetah智能体在数据集被投毒前后的性能对比进行说明。研究深入探讨了数据投毒攻击涉及的各类风险，以及从常见投毒攻击到主流数据防御方案中医疗数据伪造问题。针对安全性与可靠性需求，我们提出采用加权哈希验证与密度比加权行为克隆算法相结合的鲁棒离线强化学习方法。所提算法的四个阶段（阶段0至阶段3）围绕离线强化学习在数据投毒攻击中的安全防护机制展开阐述。最后，本文给出结论与未来研究方向，旨在将密度比加权行为克隆算法与其他数据防御策略相结合，以应对并防范未来的数据污染型网络攻击。