A Survey of Backdoor Attacks and Defenses on Neural Networks
Deep neural networks(DNNs)have experienced a remarkable surge in development and widespread application in recent years.Their ability to process large datasets and navigate complex model architectures has rendered them indispensable in a myriad of fields,ranging from image recognition to natural language understanding.However,this reliance on external resources,such as data samples and pre-trained models,during the training phase introduces a significant vulnerability to the security of neural network models.The foremost concern lies in the potential threat posed by untrustworthy third-party resources,with backdoor attacks on neural networks emerging as one of the most insidious security risks.In a backdoor attack scenario,malicious actors surreptitiously implant a backdoor into the model by manipulating either the dataset or the model architecture.This manipulation establishes a covert connection between a specific trigger pattern within the sample data and a predetermined target class.Consequently,the model misclassifies any samples containing the trigger pattern,potentially leading to severe consequences.To provide a comprehensive understanding of the principles and defense strategies against backdoor attacks on neural networks,this paper undertakes a systematic analysis of the subject matter.Initially,we delineate the four fundamental elements crucial to comprehending backdoor attacks on neural networks.Subsequently,we formulate an attack and defense model aimed at mitigating the risks associated with such attacks.This model elucidates the potential attack and defense methodologies employed across the four conventional stages of training neural networks.Furthermore,we conduct an in-depth comparative analysis of existing research from the perspectives of both backdoor attacks and defenses on neural networks.This analysis encompasses a wide array of factors,including attack/defense methods,key technologies,and application scenarios,all viewed through the lens of the capabilities of attackers and defenders.Through this comprehensive exploration,we aim to shed light on the underlying causes,potential harms,guiding principles,and mitigation strategies associated with backdoor attacks on neural networks.In conclusion,this paper not only elucidates the intricacies of backdoor attacks on neural networks but also explores the potential positive implications for future research stemming from the principle involved in such attacks.By comprehensively understanding these underlying principles,researchers can develop more robust defense mechanisms,thereby bolstering the overall security of neural network systems.Our objective is to maximize the beneficial impact of this study on the future advancement of neural network security.Moreover,by delving into the potential positive effects of the principles underlying backdoor attacks on neural networks,this paper aims to inspire novel avenues of research and innovation in the field.The examination of these principles may uncover new insights and methodologies that could lead to breakthroughs in neural network security and beyond.Ultimately,the dissemination of such knowledge has the potential to catalyze transformative advancements in the field of artificial intelligence and cybersecurity.In summary,this paper presents a comprehensive analysis of backdoor attacks on neural networks,encompassing their causes,impacts,defense strategies,and future implications.By addressing these issues,we hope to contribute to the ongoing dialogue surrounding neural network security and foster innovation in this critical domain.
deep neural networktriggerbackdoor attacksbackdoor defensesattack and defense model
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