Vinod P.

The rapid proliferation of deep neural networks (DNNs) across several domains has led to increasing concerns regarding intellectual property (IP) protection and model misuse. Trained DNNs represent valuable assets, often developed through significant investments. However, the ease with which models can be copied, redistributed, or repurposed highlights the urgent need for effective mechanisms to assert and verify model ownership. In this work, we propose an efficient and resilient white-box watermarking framework that embeds ownership information into the internal parameters of a DNN using chaotic sequences. The watermark is generated using a logistic map, a well-known chaotic function, producing a sequence that is sensitive to its initialization parameters. This sequence is injected into the weights of a chosen intermediate layer without requiring structural modifications to the model or degradation in predictive performance. To validate ownership, we introduce a verification process based on a genetic algorithm that recovers the original chaotic parameters by optimizing the similarity between the extracted and regenerated sequences. The effectiveness of the proposed approach is demonstrated through extensive experiments on image classification tasks using MNIST and CIFAR-10 datasets. The results show that the embedded watermark remains detectable after fine-tuning, with negligible loss in model accuracy. In addition to numerical recovery of the watermark, we perform visual analyses using weight density plots and construct activation-based classifiers to distinguish between original, watermarked, and tampered models. Overall, the proposed method offers a flexible and scalable solution for embedding and verifying model ownership in white-box settings well-suited for real-world scenarios where IP protection is critical.

Federated Learning (FL) has recently emerged as a revolutionary approach to collaborative training Machine Learning models. In particular, it enables decentralized model training while preserving data privacy, but its distributed nature makes it highly vulnerable to a severe attack known as Data Poisoning. In such scenarios, malicious clients inject manipulated data into the training process, thereby degrading global model performance or causing targeted misclassification. In this paper, we present a novel defense mechanism called GShield, designed to detect and mitigate malicious and low-quality updates, especially under non-independent and identically distributed (non-IID) data scenarios. GShield operates by learning the distribution of benign gradients through clustering and Gaussian modeling during an initial round, enabling it to establish a reliable baseline of trusted client behavior. With this benign profile, GShield selectively aggregates only those updates that align with the expected gradient patterns, effectively isolating adversarial clients and preserving the integrity of the global model. An extensive experimental campaign demonstrates that our proposed defense significantly improves model robustness compared to the state-of-the-art methods while maintaining a high accuracy of performance across both tabular and image datasets. Furthermore, GShield improves the accuracy of the targeted class by 43\% to 65\% after detecting malicious and low-quality clients.