mlsanityData to 7 Oct 2026

Deep Learning Backdoors

arXiv 2007.08273 ↗First posted 16 Jul 2020cs.CR cs.LGcited by 19

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Abstract (arXiv)

Intuitively, a backdoor attack against Deep Neural Networks (DNNs) is to inject hidden malicious behaviors into DNNs such that the backdoor model behaves legitimately for benign inputs, yet invokes a predefined malicious behavior when its input contains a malicious trigger. The trigger can take a plethora of forms, including a special object present in the image (e.g., a yellow pad), a shape filled with custom textures (e.g., logos with particular colors) or even image-wide stylizations with special filters (e.g., images altered by Nashville or Gotham filters). These filters can be applied to the original image by replacing or perturbing a set of image pixels.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jul 2020.

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Citing papers per year: 2021: 10; 2022: 2; 2023: 4; 2024: 2; 2025: 1.

19 papers.

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  1. 2021Backdoor Scanning for Deep Neural Networks through K-Arm Optimizationcited by 96
  2. 2021Hidden Backdoors in Human-Centric Language Modelscited by 83
  3. 2022BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learningcited by 82
  4. 2021Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-Level Backdoor Attackscited by 44
  5. 2023UNICORN: A Unified Backdoor Trigger Inversion Frameworkcited by 44
  6. 2021Can You Hear It? Backdoor Attacks via Ultrasonic Triggerscited by 41
  7. 2021The Threat of Offensive AI to Organizationscited by 37
  8. 2021Quantifying and Mitigating Privacy Risks of Contrastive Learningcited by 35
  9. 2021Dual-Key Multimodal Backdoors for Visual Question Answeringcited by 35
  10. 2024Test-Time Backdoor Attacks on Multimodal Large Language Modelscited by 35
  11. 2021Grounding Representation Similarity with Statistical Testingcited by 28
  12. 2023Analyzing And Editing Inner Mechanisms Of Backdoored Language Modelscited by 22
7 more
  1. 2023Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Surveycited by 17
  2. 2022Backdoor Vulnerabilities in Normally Trained Deep Learning Modelscited by 12
  3. 2023Two-in-One: A Model Hijacking Attack Against Text Generation Modelscited by 11
  4. 2021EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetrycited by 5
  5. 2021Oriole: Thwarting Privacy against Trustworthy Deep Learning Modelscited by 2
  6. 2024End-to-End Anti-Backdoor Learning on Images and Time Seriescited by 1
  7. 2025Char-mander Use mBackdoor! A Study of Cross-lingual Backdoor Attacks in Multilingual LLMscited by 0