  Deep Neural Network (DNN) models have vulnerabilities related to security
concerns, with attackers usually employing complex hacking techniques to expose
their structures. Data poisoning-enabled perturbation attacks are complex
adversarial ones that inject false data into models. They negatively impact the
learning process, with no benefit to deeper networks, as they degrade a model's
accuracy and convergence rates. In this paper, we propose an
attack-agnostic-based defense method for mitigating their influence. In it, a
Defensive Feature Layer (DFL) is integrated with a well-known DNN architecture
which assists in neutralizing the effects of illegitimate perturbation samples
in the feature space. To boost the robustness and trustworthiness of this
method for correctly classifying attacked input samples, we regularize the
hidden space of a trained model with a discriminative loss function called
Polarized Contrastive Loss (PCL). It improves discrimination among samples in
different classes and maintains the resemblance of those in the same class.
Also, we integrate a DFL and PCL in a compact model for defending against data
poisoning attacks. This method is trained and tested using the CIFAR-10 and
MNIST datasets with data poisoning-enabled perturbation attacks, with the
experimental results revealing its excellent performance compared with those of
recent peer techniques.
