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These days, deep learning models have achieved great success in multiplefields, from autonomous driving to medical diagnosis. These models haveexpanded the abilities of artificial intelligence by offering great solutionsto complex problems that were very difficult to solve earlier. In spite oftheir unseen success in various, it has been identified, through researchconducted, that deep learning models can be subjected to various attacks thatcompromise model security and data privacy of the Deep Neural Network models.Deep learning models can be subjected to various attacks at different stages oftheir lifecycle. During the testing phase, attackers can exploitvulnerabilities through different kinds of attacks such as Model ExtractionAttacks, Model Inversion attacks, and Adversarial attacks. Model ExtractionAttacks are aimed at reverse-engineering a trained deep learning model, withthe primary objective of revealing its architecture and parameters. Modelinversion attacks aim to compromise the privacy of the data used in the Deeplearning model. These attacks are done to compromise the confidentiality of themodel by going through the sensitive training data from the model'spredictions. By analyzing the model's responses, attackers aim to reconstructsensitive information. In this way, the model's data privacy is compromised.Adversarial attacks, mainly employed on computer vision models, are made tocorrupt models into confidently making incorrect predictions through malicioustesting data. These attacks subtly alter the input data, making it look normalbut misleading deep learning models to make incorrect decisions. Such attackscan happen during both the model's evaluation and training phases. DataPoisoning Attacks add harmful data to the training set, disrupting the learningprocess and reducing the reliability of the deep learning mode.

Security and Privacy Challenges in Deep Learning Models