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Cyber-Physical Steganography in Robotic Motion Control
Steganography, the art of information hiding, has continually evolved across visual, auditory and linguistic domains, adapting to the ceaseless interplay between steganographic concealment and steganalytic revelation.
Steganography in Game Actions
As a proof of concept, we exemplify action steganography through the game of labyrinth, a navigation task where subliminal communication is concealed within the act of steering toward a destination.
Psychometrics for Hypnopaedia-Aware Machinery via Chaotic Projection of Artificial Mental Imagery
Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences.
Agentic Copyright Watermarking against Adversarial Evidence Forgery with Purification-Agnostic Curriculum Proxy Learning
With the proliferation of AI agents in various domains, protecting the ownership of AI models has become crucial due to the significant investment in their development.
Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off
Although adversarial training has been the state-of-the-art approach to defend against adversarial examples (AEs), it suffers from a robustness-accuracy trade-off, where high robustness is achieved at the cost of clean accuracy.
Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformation
Misinformation has become a major challenge in the era of increasing digital information, requiring the development of effective detection methods.
Enhancing Robustness of LLM-Synthetic Text Detectors for Academic Writing: A Comprehensive Analysis
In this paper, we present a comprehensive analysis of the impact of prompts on the text generated by LLMs and highlight the potential lack of robustness in one of the current state-of-the-art GPT detectors.
Automation of reversible steganographic coding with nonlinear discrete optimisation
Steganography can serve as an authentication solution through the use of a digital signature embedded in a carrier object to ensure the integrity of the object and simultaneously lighten the burden of metadata management.
On the predictability in reversible steganography
The core strength of neural networks is the ability to render accurate predictions for a bewildering variety of data.
Bayesian Neural Networks for Reversible Steganography
A fundamental pillar of reversible steganography is predictive modelling which can be realised via deep neural networks.
Deep Learning for Predictive Analytics in Reversible Steganography
Given that reversibility is governed independently by the coding module, we narrow our focus to the incorporation of neural networks into the analytics module, which serves the purpose of predicting pixel intensities and a pivotal role in determining capacity and imperceptibility.
