mlsanityData to 7 Oct 2026

A Robust Attack: Displacement Backdoor Attack

arXiv 2502.10490 ↗First posted 14 Feb 2025cs.CR cs.AI cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

As artificial intelligence becomes more prevalent in our lives, people are enjoying the convenience it brings, but they are also facing hidden threats, such as data poisoning and adversarial attacks. These threats can have disastrous consequences for the application of artificial intelligence, especially for some applications that take effect immediately, such as autonomous driving and medical fields. Among these threats, backdoor attacks have left a deep impression on people with their concealment and simple deployment, making them a threat that cannot be ignored, however, in the process of deploying the backdoor model, the backdoor attack often has some reasons that make it unsatisfactory in real-world applications, such as jitter and brightness changes. Based on this, we propose a highly robust backdoor attack that shifts the target sample and combines it with itself to form a backdoor sample, the Displacement Backdoor Attack(DBA). Experimental results show that the DBA attack can resist data augmentation that simulates real-world differences, such as rotation and cropping.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 34 references that are in the map. First posted on arXiv 14 Feb 2025.

Builds on

24 of 34 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  3. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,052
  4. 2017Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoningcited by 1,012
  5. 2013Speech Recognition with Deep Recurrent Neural Networkscited by 666
  6. 2018Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networkscited by 550
  7. 2018Detecting Backdoor Attacks on Deep Neural Networks by Activation Clusteringcited by 429
  8. 2020Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networkscited by 341
  9. 2020Invisible Backdoor Attack with Sample-Specific Triggerscited by 338
  10. 2021Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networkscited by 313
  11. 2020Input-Aware Dynamic Backdoor Attackcited by 304
  12. 2019Label-Consistent Backdoor Attackscited by 285
12 more
  1. 2021Anti-Backdoor Learning: Training Clean Models on Poisoned Datacited by 258
  2. 2019A new Backdoor Attack in CNNs by training set corruption without label poisoningcited by 237
  3. 2020Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustnesscited by 130
  4. 2019Transferable Clean-Label Poisoning Attacks on Deep Neural Netscited by 129
  5. 2020Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Casescited by 93
  6. 2020Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoffcited by 86
  7. 2019Defending Neural Backdoors via Generative Distribution Modelingcited by 82
  8. 2020DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentationcited by 75
  9. 2023Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimizationcited by 60
  10. 2020Computing Systems for Autonomous Driving: State-of-the-Art and Challengescited by 47
  11. 2021Few-shot Backdoor Defense Using Shapley Estimationcited by 40
  12. 2022Fine-Tuning Is All You Need to Mitigate Backdoor Attackscited by 35

Built on by

Not cited yet.