AIMC Topic: Deception

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Protecting world leaders against deep fakes using facial, gestural, and vocal mannerisms.

Proceedings of the National Academy of Sciences of the United States of America
Since their emergence a few years ago, artificial intelligence (AI)-synthesized media-so-called deep fakes-have dramatically increased in quality, sophistication, and ease of generation. Deep fakes have been weaponized for use in nonconsensual pornog...

Recursive Minimum-Variance Filter Design for State-Saturated Complex Networks With Uncertain Coupling Strengths Subject to Deception Attacks.

IEEE transactions on cybernetics
In this article, the recursive filtering problem is investigated for state-saturated complex networks (CNs) subject to uncertain coupling strengths (UCSs) and deception attacks. The measurement signals transmitted via the communication network may su...

IAT faking indices revisited: Aspects of replicability and differential validity.

Behavior research methods
Research demonstrates that IATs are fakeable. Several indices [either slowing down or speeding up, and increasing errors or reducing errors in congruent and incongruent blocks; Combined Task Slowing (CTS); Ratio 150-10000] have been developed to dete...

Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions.

Sensors (Basel, Switzerland)
With information systems worldwide being attacked daily, analogies from traditional warfare are apt, and deception tactics have historically proven effective as both a strategy and a technique for Defense. Defensive Deception includes thinking like a...

Event-Triggered Output Feedback Synchronization of Master-Slave Neural Networks Under Deception Attacks.

IEEE transactions on neural networks and learning systems
The problem of event-triggered synchronization of master-slave neural networks is investigated in this article. It is assumed that both communication channels from the sensor to controller and from controller to actuator are subject to stochastic dec...

Event-triggered H/passive synchronization for Markov jumping reaction-diffusion neural networks under deception attacks.

ISA transactions
The issue of H/passive master-slave synchronization for Markov jumping neural networks with reaction-diffusion terms is investigated in this paper via an event-triggered control scheme under deception attacks. To lighten the burden of limited communi...

Deepfake Detection Using the Rate of Change between Frames Based on Computer Vision.

Sensors (Basel, Switzerland)
Recently, artificial intelligence has been successfully used in fields, such as computer vision, voice, and big data analysis. However, various problems, such as security, privacy, and ethics, also occur owing to the development of artificial intelli...

Deceptive Online Content Detection Using Only Message Characteristics and a Machine Learning Trained Expert System.

Sensors (Basel, Switzerland)
This paper considers the use of a post metadata-based approach to identifying intentionally deceptive online content. It presents the use of an inherently explainable artificial intelligence technique, which utilizes machine learning to train an expe...

Exponential synchronization of coupled neural networks under stochastic deception attacks.

Neural networks : the official journal of the International Neural Network Society
In this paper, the issue of synchronization is investigated for coupled neural networks subject to stochastic deception attacks. Firstly, a general differential inequality with delayed impulses is given. Then, the established differential inequality ...

Machine learning based approach to exam cheating detection.

PloS one
The COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of dir...