# A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

arXiv: https://arxiv.org/abs/2307.06659 · HTML: https://mlsanity.com/paper/2307.06659/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 174 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 20 of 20)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Adversarial Attacks and Defences: A Survey](https://mlsanity.com/paper/1810.00069/) (2018)
- [Does Object Recognition Work for Everyone?](https://mlsanity.com/paper/1906.02659/) (2019)
- [Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection](https://mlsanity.com/paper/2203.15269/) (2022)
- [Learning to Adversarially Blur Visual Object Tracking](https://mlsanity.com/paper/2107.12085/) (2021)
- [It GAN DO Better: GAN-based Detection of Objects on Images with Varying Quality](https://mlsanity.com/paper/1912.01707/) (2019)
- [Smart Traffic Monitoring System using Computer Vision and Edge Computing](https://mlsanity.com/paper/2109.03141/) (2021)
- [Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements and Challenges](https://mlsanity.com/paper/2007.12874/) (2020)
- [Blockchain for Edge of Things: Applications, Opportunities, and Challenges](https://mlsanity.com/paper/2110.05022/) (2021)
- [Monitoring crop phenology with street-level imagery using computer vision](https://mlsanity.com/paper/2112.09190/) (2021)
- [Spatial Monitoring and Insect Behavioural Analysis Using Computer Vision for Precision Pollination](https://mlsanity.com/paper/2205.04675/) (2022)
- [An Amateur Drone Surveillance System Based on Cognitive Internet of Things](https://mlsanity.com/paper/1711.10738/) (2017)
- [A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions](https://mlsanity.com/paper/2009.00858/) (2020)
- [Reconfigurable Cyber-Physical System for Critical Infrastructure Protection in Smart Cities via Smart Video-Surveillance](https://mlsanity.com/paper/2011.14416/) (2020)
- [Recent advances in Blockchain Technology: A survey on Applications and Challenges](https://mlsanity.com/paper/2009.05718/) (2020)
- [Trends in crypto-currencies and blockchain technologies: A monetary theory and regulation perspective](https://mlsanity.com/paper/1508.04364/) (2015)
- [UAS Imagery and Computer Vision for Site-Specific Weed Control in Corn](https://mlsanity.com/paper/2204.12417/) (2022)
- [Wildfire Smoke Detection with Computer Vision](https://mlsanity.com/paper/2301.05070/) (2023)

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

- [Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain](https://mlsanity.com/paper/2409.16444/) (2024)

## Abstract (arXiv)

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed.