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ASIN : 1492044954
Publisher : O’Reilly Media; 1st edition (September 3, 2019)
Language : English
Paperback : 244 pages
ISBN-10 : 9781492044956
ISBN-13 : 978-1492044956
Item Weight : 14.4 ounces
Dimensions : 7 x 0.5 x 9.1 inches
In recent years, deep neural networks have become increasingly popular for a wide range of applications, from image recognition to natural language processing. However, these powerful algorithms are not without their vulnerabilities. One major concern is their susceptibility to adversarial attacks, where small, carefully crafted perturbations to input data can cause the model to make incorrect predictions.
To address this issue, researchers are exploring various techniques to make deep neural networks more robust and less susceptible to adversarial trickery. One approach is to incorporate adversarial training into the learning process, where the model is exposed to adversarially perturbed examples during training to help it learn to better generalize to such inputs. Another strategy is to use defensive techniques such as input preprocessing, gradient masking, and adversarial training to enhance the model’s robustness.
By incorporating these techniques and continually researching new methods to strengthen deep neural networks, we can make significant progress towards building AI systems that are less vulnerable to adversarial attacks. This will not only help improve the reliability and trustworthiness of AI systems but also pave the way for more widespread and secure deployment of AI technologies in various fields.
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