1,348 research outputs found
DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks
This paper proposes DeepMarks, a novel end-to-end framework for systematic
fingerprinting in the context of Deep Learning (DL). Remarkable progress has
been made in the area of deep learning. Sharing the trained DL models has
become a trend that is ubiquitous in various fields ranging from biomedical
diagnosis to stock prediction. As the availability and popularity of
pre-trained models are increasing, it is critical to protect the Intellectual
Property (IP) of the model owner. DeepMarks introduces the first fingerprinting
methodology that enables the model owner to embed unique fingerprints within
the parameters (weights) of her model and later identify undesired usages of
her distributed models. The proposed framework embeds the fingerprints in the
Probability Density Function (pdf) of trainable weights by leveraging the extra
capacity available in contemporary DL models. DeepMarks is robust against
fingerprints collusion as well as network transformation attacks, including
model compression and model fine-tuning. Extensive proof-of-concept evaluations
on MNIST and CIFAR10 datasets, as well as a wide variety of deep neural
networks architectures such as Wide Residual Networks (WRNs) and Convolutional
Neural Networks (CNNs), corroborate the effectiveness and robustness of
DeepMarks framework
DNA Steganalysis Using Deep Recurrent Neural Networks
Recent advances in next-generation sequencing technologies have facilitated
the use of deoxyribonucleic acid (DNA) as a novel covert channels in
steganography. There are various methods that exist in other domains to detect
hidden messages in conventional covert channels. However, they have not been
applied to DNA steganography. The current most common detection approaches,
namely frequency analysis-based methods, often overlook important signals when
directly applied to DNA steganography because those methods depend on the
distribution of the number of sequence characters. To address this limitation,
we propose a general sequence learning-based DNA steganalysis framework. The
proposed approach learns the intrinsic distribution of coding and non-coding
sequences and detects hidden messages by exploiting distribution variations
after hiding these messages. Using deep recurrent neural networks (RNNs), our
framework identifies the distribution variations by using the classification
score to predict whether a sequence is to be a coding or non-coding sequence.
We compare our proposed method to various existing methods and biological
sequence analysis methods implemented on top of our framework. According to our
experimental results, our approach delivers a robust detection performance
compared to other tools
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