1,412 research outputs found
Steganography: a class of secure and robust algorithms
This research work presents a new class of non-blind information hiding
algorithms that are stego-secure and robust. They are based on some finite
domains iterations having the Devaney's topological chaos property. Thanks to a
complete formalization of the approach we prove security against watermark-only
attacks of a large class of steganographic algorithms. Finally a complete study
of robustness is given in frequency DWT and DCT domains.Comment: Published in The Computer Journal special issue about steganograph
Augmented watermarking
This thesis provides an augmented watermarking technique wherein noise is based on the watermark added to the watermarked image so that only the end user who has the key for embedding the watermark can both remove the noise and watermark to get a final clear image. The recovery for different values of noise is observed. This system may be implemented as a basic digital rights management system by defining a regime of partial rights using overlaid watermarks, together with respectively added layers of noise, in which the rights of the users define the precision with which the signals may be viewed
Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things
Securing the Internet of Things (IoT) is a necessary milestone toward
expediting the deployment of its applications and services. In particular, the
functionality of the IoT devices is extremely dependent on the reliability of
their message transmission. Cyber attacks such as data injection,
eavesdropping, and man-in-the-middle threats can lead to security challenges.
Securing IoT devices against such attacks requires accounting for their
stringent computational power and need for low-latency operations. In this
paper, a novel deep learning method is proposed for dynamic watermarking of IoT
signals to detect cyber attacks. The proposed learning framework, based on a
long short-term memory (LSTM) structure, enables the IoT devices to extract a
set of stochastic features from their generated signal and dynamically
watermark these features into the signal. This method enables the IoT's cloud
center, which collects signals from the IoT devices, to effectively
authenticate the reliability of the signals. Furthermore, the proposed method
prevents complicated attack scenarios such as eavesdropping in which the cyber
attacker collects the data from the IoT devices and aims to break the
watermarking algorithm. Simulation results show that, with an attack detection
delay of under 1 second the messages can be transmitted from IoT devices with
an almost 100% reliability.Comment: 6 pages, 9 figure
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