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Deepfake Generation for Use in Dicti...
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Barros, Vasco Mucha,
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Deepfake Generation for Use in Dictionary Attacks on Facial Recognition Systems /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Deepfake Generation for Use in Dictionary Attacks on Facial Recognition Systems // Vasco Mucha Barros.
作者:
Barros, Vasco Mucha,
面頁冊數:
1 electronic resource (69 pages)
附註:
Source: Masters Abstracts International, Volume: 86-02.
Contained By:
Masters Abstracts International86-02.
標題:
Communication channels. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31161353
ISBN:
9798383778876
Deepfake Generation for Use in Dictionary Attacks on Facial Recognition Systems /
Barros, Vasco Mucha,
Deepfake Generation for Use in Dictionary Attacks on Facial Recognition Systems /
Vasco Mucha Barros. - 1 electronic resource (69 pages)
Source: Masters Abstracts International, Volume: 86-02.
Biometric authentication, although not new, is seeing an increase in popularity and usage in current times, particularly facial recognition. At the same time, image manipulation technology is also booming, with software becoming accessible to virtually anyone who has an above-average machine. Deep learning, though deepfakes, is already a cause of concern due to its usage in manipulated content such as fake news, in an era of digital information. As facial recognition becomes more prominent in people's daily lives, the chances for deepfakes to be used maliciously in that another field should not be ignored.In order to analyze the extents of the vulnerabilities of these systems, we propose training a Generative Adversarial Network that generates a particular type of deepfake, and then using those fakes, testing whether or not various systems recognize them as real faces and furthermore whether they can or not breach the systems' security. The reasoning for the specific particularity of the fakes is discussed further ahead.In addition to that, we discuss the concerning results produced by some of the previous works on this topic, as well as how those could still be taken even further if different attacking angles are combined into a single attack. To contrast, we also talk about anti-deepfake tools and how they can prove to be crucial to combat these new type of threats to facial authentication systems.
English
ISBN: 9798383778876Subjects--Topical Terms:
3560268
Communication channels.
Deepfake Generation for Use in Dictionary Attacks on Facial Recognition Systems /
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Biometric authentication, although not new, is seeing an increase in popularity and usage in current times, particularly facial recognition. At the same time, image manipulation technology is also booming, with software becoming accessible to virtually anyone who has an above-average machine. Deep learning, though deepfakes, is already a cause of concern due to its usage in manipulated content such as fake news, in an era of digital information. As facial recognition becomes more prominent in people's daily lives, the chances for deepfakes to be used maliciously in that another field should not be ignored.In order to analyze the extents of the vulnerabilities of these systems, we propose training a Generative Adversarial Network that generates a particular type of deepfake, and then using those fakes, testing whether or not various systems recognize them as real faces and furthermore whether they can or not breach the systems' security. The reasoning for the specific particularity of the fakes is discussed further ahead.In addition to that, we discuss the concerning results produced by some of the previous works on this topic, as well as how those could still be taken even further if different attacking angles are combined into a single attack. To contrast, we also talk about anti-deepfake tools and how they can prove to be crucial to combat these new type of threats to facial authentication systems.
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