With continual advancements in deep learning, the potential misuse of deepfake is increasing and its detection is in a major scope of work. A model is trained to recognize the patterns in input data, and deepfake recognizes those patterns in a fabricated way. Sometimes a small, intentional change is added in the data points, and these changes are undetectable to humans and confuse the learning model. Those changes are called adversarial perturbations. Compressive adversarial perturbations aim to make those changes even smaller and harder to detect. Authors explore a sophisticated framework—Compressive Adversarial Perturbations and Detection (ComPAD) which is used to detect adversarial attacks. This paper explores the strategies and provides comparative analysis of methods used by different researchers. Various datasets including UADFV, DeepfakeTIMIT, LFW, FF++, and Deeperforensics are evaluated to achieve the highest metrics. Methods based on convolutional neural networks, particle swarm optimization, genetic algorithm, and Disjoint Diffusion Deep Face Detection (D4) are used for detection. Authors also discuss the challenges such as generalization of models across the new data, the continuous evolution of adversarial perturbations that lead to consistent attacks, and the scalability issues for the real-time deepfake. Concluding that models can significantly improve the accuracy, robustness, and generalization.