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Gartner发布新兴技术指南:生成式人工智能和深度伪造对身份验证的影响
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2024-03-28
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使用生成式人工智能(GenAI)技术生成的 Deepfakes(深度伪造) 对身份验证的完整性构成了根本威胁。身份验证产品领导者必须了解这一新兴威胁,并采取积极主动的方法来区分和保护其解决方案产品。 活体检测技术对于防御深度伪造以及在身份验证过程的“自拍捕获步骤”期间验证个人用户的真实存在变得至关重要。这促使供应商结合多种因素来区分其解决方案并提供更全面的保护。 生成式人工智能的最新进展使深度伪造变得越来越复杂和适应性更强,因为高级攻击者现在可以以惊人的精度模仿面部表情、眨眼模式甚至微妙的微动作,甚至混淆了最先进的检测算法。身份验证领域的产品领导者正在被迫采用更全面的方法,其中结合了多层防御策略来防御深度伪造。 深度伪造攻击者正在将 GenAI 的快速发展武器化,不断发明新的、更复杂的攻击技术。随着 GenAI 的不断快速发展,身份验证产品领导者将需要积极与人工智能和安全专家合作,以预测未来的攻击向量并主动制定对策。
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Gartner, Inc. | G00803486
Page 1 of 12
Emerging Tech: The Impact of AI and Deepfakes
on Identity Verification
Published 8 February 2024 - ID G00803486 - 17 min read
By Analyst(s): Swati Rakheja, Akif Khan
Initiatives: Emerging Technologies and Trends Impact on Products and Services
Deepfakes generated using generative AI technologies pose a
fundamental threat to the integrity of identity verification. Identity
verification product leaders must understand this emerging threat
and take a proactive approach to differentiate and secure their
solution offerings.
Overview
Key Findings
Liveness detection technologies are becoming critical for defending against
deepfakes and verifying the genuine presence of an individual user during the “selfie
capture step” of the identity verification process. This is driving vendors to use a
combination of multiple factors to differentiate their solution offerings and offer
more comprehensive protection.
■
Recent advancements in generative AI (GenAI) are making deepfakes increasingly
sophisticated and adaptable as advanced attackers can now mimic facial
expressions, blinking patterns and even subtle micromovements with uncanny
accuracy, confounding even the most advanced detection algorithms. Product
leaders in the identity verification space are being driven to adopt a more holistic
approach that incorporates a multilayered defense strategy to defend against
deepfakes.
■
Deepfake attackers are weaponizing the rapid evolution of GenAI, constantly
inventing new and more sophisticated attack techniques. As GenAI continues to
rapidly evolve, identity verification product leaders will need to actively engage with
AI and security experts to anticipate future attack vectors and proactively develop
countermeasures.
■
This research note is restricted to the personal use of liuyang17@qianxin.com.
Gartner, Inc. | G00803486
Page 2 of 12
Recommendations
To defend against these rising deepfake attacks, identity verification product leaders
must:
Strategic Planning Assumption
By 2026, attacks using AI-generated deepfakes on face biometrics will mean that 30% of
enterprises will no longer consider such identity verification and authentication solutions
to be reliable in isolation.
Analysis
Technology Description
GenAI technologies can generate new derived versions of content, strategies, designs and
methods by learning from large repositories of original source content. GenAI can have
profound impacts on various aspects of business, including content discovery, creation,
authenticity and regulations; automation of human work; and the customer and employee
experience (see Emerging Tech: Primary Impact of Generative AI on Business Use Cases).
Invest in the development and implementation of a combination of active and
passive liveness detection strategies to assess genuine presence and detect
deepfakes, with a strategic focus on passive liveness detection as AI tech matures
and attacks become more sophisticated.
■
Integrate detection capabilities for additional signals indicating an attack, choosing
between in-house development or partnerships/mergers and acquisitions (M&As)
with existing vendors by evaluating the level of product maturity and
commoditization.
■
Invest in a threat intelligence team focused on tracking emerging deepfake-related
threats and collecting intelligence on various techniques being used by attackers.
Additionally, product leaders should leverage GenAI to their benefit, using synthetic
data to strengthen machine learning (ML) algorithm training.
■
This research note is restricted to the personal use of liuyang17@qianxin.com.
Gartner, Inc. | G00803486
Page 3 of 12
The advent of GenAI has increased the sophistication of attacks that identity verification
vendors must defend against. GenAI tools are capable of producing seemingly real
content in voice, video and image format with minimal technical input, and deepfake
misuse can subvert the verification process. Though deepfakes have existed for some
time, the proliferation of user-friendly tools has made their creation more readily
accessible, even to individuals with limited technical proficiency. The number of
deepfakes detected worldwide in 2023
1
was 10 times the number detected in 2022.
Gartner estimates the time to reach the early majority (i.e., more than 16% target market
adoption) for deepfakes is one to three years because deepfakes go hand-in-hand with the
GenAI advances that underpin their creation. (See Emerging Tech Impact Radar: Artificial
Intelligence). This requires that identity verification vendors take a multipronged approach
to safeguard against these rising deepfake attacks.
Market Definition
Gartner defines identity verification as the combination of activities during a remote
interaction that brings a real-world identity claim within organizational risk tolerances.
Identity verification capabilities, delivered as SaaS or on-premises, provide the assurance
that a real-world identity exists and that the individual claiming the identity is its true
owner and is genuinely present during a remote interaction.
This typically involves a person capturing a real-time image of their photo identity
document, which the tool inspects for signs of counterfeit or forgery. Once the authenticity
of the document is established, the person is prompted to capture a photo or a video clip
of their face. During this step, the tool establishes the genuine presence of the person
using liveness detection (or, formally, presentation attack detection), followed by biometric
facial comparison with the photo from the identity document.
Some identity verification vendors also capture voiceprint during the verification process,
to be leveraged in the future for the purposes of contact-center voice authentication. That
user flow is susceptible to voice deepfakes, and some vendors are investing in real-time
voice deepfake detection capabilities as well. However, for the purpose of this research
note, we have focused on deepfakes targeting selfies or the video capture process during
identity verification. Figure 1 summarizes the critical insights for deepfake detection
discussed in this document.
This research note is restricted to the personal use of liuyang17@qianxin.com.
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