https://okokprojects.com/ IEEE PROJECTS 2023-2024 TITLE LIST WhatsApp : +91-8144199666 From Our Title List the Cost will be, Mail Us: okokprojects@gmail.com Website: : https://www.okokprojects.com : http://www.ieeeproject.net Support Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Video Tutorials * Supporting Softwares Support Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * Remote Connectivity * Document Customization * Live Chat Support

1. DefQ: Defensive Quantization Against
Inference Slow
Computing
Abstract
The novel multiexit deep neural network (DNN) architectures provide a new
optimization solution for efficient model inference in edge systems. Inference
of most samples can be completed within the first few layers on an edge
device without the need to transmit them to a remo
significantly increase the inference speed and system throughput, which is
particularly beneficial to the resource
researchers proposed an inference slow
where an external adversary can add imperceptible perturbations on clean
samples to invalidate the multiexit mechanism. In this article, we propose a
defensive quantization ( DefQ ) method as the first defense against the
inference slow-down attack. It is designed
DefQ: Defensive Quantization Against
Inference Slow-Down Attack for Edge
deep neural network (DNN) architectures provide a new
optimization solution for efficient model inference in edge systems. Inference
of most samples can be completed within the first few layers on an edge
device without the need to transmit them to a remote server. This can
significantly increase the inference speed and system throughput, which is
particularly beneficial to the resource-constrained scenarios. Unfortunately,
researchers proposed an inference slow-down attack against this technique,
external adversary can add imperceptible perturbations on clean
samples to invalidate the multiexit mechanism. In this article, we propose a
defensive quantization ( DefQ ) method as the first defense against the
down attack. It is designed to be lightweight and can be easily
DefQ: Defensive Quantization Against
Down Attack for Edge
deep neural network (DNN) architectures provide a new
optimization solution for efficient model inference in edge systems. Inference
of most samples can be completed within the first few layers on an edge
te server. This can
significantly increase the inference speed and system throughput, which is
constrained scenarios. Unfortunately,
down attack against this technique,
external adversary can add imperceptible perturbations on clean
samples to invalidate the multiexit mechanism. In this article, we propose a
defensive quantization ( DefQ ) method as the first defense against the
to be lightweight and can be easily

2. implemented in off-the-shelf camera sensors. Particularly, DefQ introduces a
novel quantization operation to preprocess the input images. It is capable of
removing the perturbations from the malicious samples and preserving the
correct inference exit points and prediction accuracy. Meanwhile, it has little
impact on the clean samples. Extensive evaluations show that DefQ can
effectively defeat the inference slow-down attack and well protect the
efficiency of edge systems.