31 research outputs found
Generative Adversarial Networks for Classic Cryptanalysis
The necessity of protecting critical information has been understood for millennia. Although classic ciphers have inherent weaknesses in comparison to modern ciphers, many classic ciphers are extremely challenging to break in practice. Machine learning techniques, such as hidden Markov models (HMM), have recently been applied with success to various classic cryptanalysis problems. In this research, we consider the effectiveness of the deep learning technique CipherGAN---which is based on the well- established generative adversarial network (GAN) architecture---for classic cipher cryptanalysis. We experiment extensively with CipherGAN on a number of classic ciphers, and we compare our results to those obtained using HMMs
Learning to Read by Spelling: Towards Unsupervised Text Recognition
This work presents a method for visual text recognition without using any
paired supervisory data. We formulate the text recognition task as one of
aligning the conditional distribution of strings predicted from given text
images, with lexically valid strings sampled from target corpora. This enables
fully automated, and unsupervised learning from just line-level text-images,
and unpaired text-string samples, obviating the need for large aligned
datasets. We present detailed analysis for various aspects of the proposed
method, namely - (1) impact of the length of training sequences on convergence,
(2) relation between character frequencies and the order in which they are
learnt, (3) generalisation ability of our recognition network to inputs of
arbitrary lengths, and (4) impact of varying the text corpus on recognition
accuracy. Finally, we demonstrate excellent text recognition accuracy on both
synthetically generated text images, and scanned images of real printed books,
using no labelled training examples
TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer
In this work, we address the problem of musical timbre transfer, where the
goal is to manipulate the timbre of a sound sample from one instrument to match
another instrument while preserving other musical content, such as pitch,
rhythm, and loudness. In principle, one could apply image-based style transfer
techniques to a time-frequency representation of an audio signal, but this
depends on having a representation that allows independent manipulation of
timbre as well as high-quality waveform generation. We introduce TimbreTron, a
method for musical timbre transfer which applies "image" domain style transfer
to a time-frequency representation of the audio signal, and then produces a
high-quality waveform using a conditional WaveNet synthesizer. We show that the
Constant Q Transform (CQT) representation is particularly well-suited to
convolutional architectures due to its approximate pitch equivariance. Based on
human perceptual evaluations, we confirmed that TimbreTron recognizably
transferred the timbre while otherwise preserving the musical content, for both
monophonic and polyphonic samples.Comment: 17 pages, published as a conference paper at ICLR 201