## 1 Introduction  
Despite the popularity of machine learning models over the past decades, there is a risk of basic functions being disrupted by different attacks. Adversarial attacks have been gaining in popularity due to their simplistic attack generation. An attacker can produce these attacks by adding subtle noise to input data to make the input malicious. This malicious input, whether image, audio, video, or text, results in a normal sample that most machine learning models misclassify without any adversarial defense [1, 2, 3, 4, 5, 6, 7, 8, 9]. The effects of adversarial attacks can even be extended to areas such as numerical simulation and stability [10, 11]. Adversarial training aims to resolve the security flaws many machine learning models face due to the malicious samples mentioned above [2, 9, 12, 13, 14, 15]. Even though various methods have been proposed to defend models from these attacks, adversarial training is by far the most robust solution to the problem. Training a model adversarially requires augmentation of adversarial examples made through different methods and adding them to the training dataset. This training method works well if the malicious sample created by the attacker uses the same or similar methods to those used when generating the augmented adversarial dataset. There can be issues if an attacker uses a different attack method to create malicious samples. When a model is not trained on a specific type of adversarial example, it fails to defend itself from the attack [16; 17]. [PERSON] et al., researchers in the field of adversarial attacks and defenses, have shown in their research [16] that it is possible to easily circumvent state-of-the-art adversarial defenses, providing evidence of the need for better defenses against adversarial attacks. Even though the authors did not explain why this phenomenon occurs, it can be hypothesized that it is due to holes in adversarial defenses created through adversarial training that allow attackers the chance of exploiting them. The process of adversarially training a model aims to teach it to recognize data samples even though there is a perturbation in the input sample. This depends entirely on the adversarial examples used to train it. Supposing that a model is provided with an adversarial sample that it was not trained to defend against, it cannot defend itself, which is a weakness in the current adversarial training process. Using one type of adversarial attack for training does not defend the model from the plethora of other attacks that can be exploited. Considering that over a thousand papers have already been published on the topic, it is clear that a universal defense is more necessary than ever [18]. This article's primary focus is showing that generative models can aid in the process of adversarial training by generating unrestricted adversarial examples, as mentioned in [19; 20]. In this way, models can be trained on adversarial samples that are not confined to \"narrow\" distributions that traditional methods such as the Fast Gradient Sign Method [1], Projected Gradient Descent [9], Basic Iterative Method [21], and Jacobian-based Saliency Maps Attacks [22] usually generate. Unrestricted adversarial examples from generative networks provide a potential solution, as these samples are generated from scratch. If visualized on a decision boundary, traditional adversarial examples are close to the model's decision boundary. In contrast, unrestricted examples contain samples close to the model boundary and further away. In this paper, we propose that the approach of creating unrestricted samples from generative models has the potential to create a universal defense. Throughout this paper, the proposed technique is explored in depth with a conceptual explanation, experimental setup, results, and analysis of the outcome.