# Global Clipper: Enhancing Safety and Reliability of Transformer-based Object Detection Models

arXiv: https://arxiv.org/abs/2406.03229 · HTML: https://mlsanity.com/paper/2406.03229/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 19 of 19)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR](https://mlsanity.com/paper/2303.07335/) (2023)
- [FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation](https://mlsanity.com/paper/1912.00941/) (2019)
- [FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks](https://mlsanity.com/paper/2003.12203/) (2020)
- [HarDNN: Feature Map Vulnerability Evaluation in CNNs](https://mlsanity.com/paper/2002.09786/) (2020)
- [A Systematic Survey of General Sparse Matrix-Matrix Multiplication](https://mlsanity.com/paper/2002.11273/) (2020)
- [Soft Error Reliability Analysis of Vision Transformers](https://mlsanity.com/paper/2302.10468/) (2023)
- [Towards a Safety Case for Hardware Fault Tolerance in Convolutional Neural Networks Using Activation Range Supervision](https://mlsanity.com/paper/2108.07019/) (2021)
- [Hardware faults that matter: Understanding and Estimating the safety impact of hardware faults on object detection DNNs](https://mlsanity.com/paper/2209.03225/) (2022)
- [A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks](https://mlsanity.com/paper/2310.20349/) (2023)
- [A Uniform Framework for Anomaly Detection in Deep Neural Networks](https://mlsanity.com/paper/2110.03092/) (2021)
- [BEA: Revisiting anchor-based object detection DNN using Budding Ensemble Architecture](https://mlsanity.com/paper/2309.08036/) (2023)
- [Large-Scale Application of Fault Injection into PyTorch Models -- an Extension to PyTorchFI for Validation Efficiency](https://mlsanity.com/paper/2310.19449/) (2023)

## Built on by (most cited first, 1 of 1)

- [On the Resilience of Text-to-Video Diffusion Models to Hardware Faults](https://mlsanity.com/paper/2608.29598/) (2026)

## Abstract (arXiv)

As transformer-based object detection models progress, their impact in critical sectors like autonomous vehicles and aviation is expected to grow. Soft errors causing bit flips during inference have significantly impacted DNN performance, altering predictions. Traditional range restriction solutions for CNNs fall short for transformers. This study introduces the Global Clipper and Global Hybrid Clipper, effective mitigation strategies specifically designed for transformer-based models. It significantly enhances their resilience to soft errors and reduces faulty inferences to ~ 0%. We also detail extensive testing across over 64 scenarios involving two transformer models (DINO-DETR and Lite-DETR) and two CNN models (YOLOv3 and SSD) using three datasets, totalling approximately 3.3 million inferences, to assess model robustness comprehensively. Moreover, the paper explores unique aspects of attention blocks in transformers and their operational differences from CNNs.