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Music generation with artificial intelligence (AI) was first explored by a Soviet mathematician Rudolf Zaripov in his 1960 study “On algorithmic description of process of music composition”. In it, he described a simple algorithm of generating a music piece: It should follow a three-part
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Emergence of Generative Artificial Intelligence (GenAI) allowed automating numerous tasks: drawing pictures, generating large volumes of text, writing programming code, assessing its quality, and so on. Natural Language Processing (NLP) especially benefits from GenAI’s
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Replay attacks are a type of Presentation Attacks (PAs), in which stolen media files are replayed from a portable device to fool a biometric authentication system. Attack tools include data, which belongs to a legitimate user and retains liveness of their voice, but is captured and used
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Antispoofing — The Key to Safety in Digital and Real Worlds
Antispoofing is an amalgam of machine learning, anatomical studies, and behavioral psychology, which solves one essential issue: keeping people’s assets and private information safe in the era when biometric data can easily be peeped and replicated.
Our encyclopedia is dedicated to the Liveness detection, Spoofing attacks and Antispoofing measures. Spoofing is a set of malicious techniques, which allow an attacker to pose as someone else, fabricate liveness cues, or synthesize a completely falsified, non-existent person from scratch to gain access to sensitive data, target’s money, device or vehicle control, and so on. Spoofing attacks have become an especially common practice since the advent of mobile technology. Internet of Things, face recognition and other systems are also quite vulnerable in the light of this threat.Our goal is to select, review and propose the most effective and promising remedies against liveness spoofing.
Spoofing attack is a malicious practice, in which a biometric trait of a real, living person — voice, fingerprint, face, or even heart rate — is replicated to fool a biometrics-based security system.
In face liveness classification, obfuscation is a malevolent technique, which allows concealing a culprit’s true identity from a biometric recognition system based on both passive or active liveness detection. In essence, obfuscation is somewhat close to Presentation Attacks (PAs)
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In liveness taxonomy, Adversarial Spoofing Attacks (AXs) refer to a malicious technique, in which spoofing data is presented as genuine to the authentication system. This type of attack exploits the lack of robustness in a Deep Neural Network (DNN), which is responsible for
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Liveness detection challenges are public competitions organized to expose vulnerabilities of the biometric systems, attest the most promising algorithms and models, adapt them to hostile and uncontrolled environments, as well as introduce training and testing datasets. Facial
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The Criminal Methods
Together with that, professional culprits will adopt more sophisticated methods to bypass an antispoofing system. There’s a rich cornucopia of tactics, tools, and tricks to emulate liveness. A motivated fraudster is ready to invest months in social engineering, collecting training data to produce a high-quality deepfake, attempting reverse engineering or trying out novel attack methods.
These techniques at times show immense creativity. For example, liveness mimicry can be achieved with simple and easy-to-obtain components:
- 1Playdough.
- 2Body paint.
- 3Gelatin-based candy.
- 4Glue and construction paper.
- 5Many other widespread items.
Even though being cheap to produce, they sometimes succeed at bypassing even advanced security systems.
In a more challenging scenario, fraudsters basically resort to extreme solutions. These can include applying advanced movie-level makeup, producing highly realistic 3D masks that emulate body heat, severing a deceased person’s finger/extracting an eye, and even undergoing a plastic surgery.
Fake audio, in most cases, refers to a falsified audio data that impersonates the voice of a legitimate speaker. Although it may not be produced for ill-intentioned purposes, synthetic audio files made with voice-cloning tools are often used for fraud and misinformation
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Essentially, biometric spoofing combines two elements: a) Production and b) Presentation. The production stage implies creation of a synthetic artifact that imitates a certain biometric trait such as voice, face, fingerprint, eye retina, etc. The presentation stage includes direct
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Importance of tackling fake media
Fake media detection is also an essential part of antispoofing. If not properly addressed and timely prevented, it can result in disastrous consequences: social instability, public opinion manipulation, defamation, etc. Plus, sociology experts indicate two negative phenomena induced by the rise of fake media: liar’s dividend and reality apathy.
Antispoofing techniques and liveness detection solutions are designed with all of these threats in mind. They encompass a wide range of concepts, ideas and visions. Voice, retina, iris, fingerprint, and facial recognition employ liveness parameters to tell a real person from a perpetrator, which is the main goal of antispoofing as such.
The antispoof checking analyzes the input data. Among which are
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Pop noises.
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Face coloring.
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Light distribution.
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Depth and geometry.
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General liveness signals
They help spot fake body parts, facial alterations, replay attacks and synthesized audiovisuals. Detecting them is the main goal of antispoofing as such.
In our articles, we discuss the most successful methods and the best antispoofing tools, as well as liveness detection techniques. We also focus on the international antispoofing and liveness standards, attack types and their classification, terminology, and other aspects of the matter. To validate the data presented, we quote scientific publications, as well as popular media, and news dedicated to liveness security and antispoofing.