Pseudo-random number generators (PRNG) are a fundamental element of many security algorithms. We introduce a novel approach to their implementation, by proposing the use of generative adversarial networks (GAN) to train a neural network to behave as a PRNG. Furthermore, we showcase a number of interesting modifications to the standard GAN architecture. The most significant is partially concealing the output of the GAN's generator, and training the adversary to discover a mapping from the overt part to the concealed part. The generator therefore learns to produce values the adversary cannot predict, rather than to approximate an explicit reference distribution. We demonstrate that a GAN can effectively train even a small feed-forward fully connected neural network to produce pseudo-random number sequences with good statistical properties. At best, subjected to the NIST test suite, the trained generator passed around 99 of test instances and 98 of overall tests, outperforming a number of standard non-cryptographic PRNGs.
Few attempts have been made to produce pseudo-random number sequences with neural networks @cite_3 @cite_8 @cite_1 @cite_4 . The most successful approaches have been presented by Tirdad and Sadeghian @cite_1 , and by @cite_4 . The former employed Hopfield neural networks adapted so as to prevent convergence and encourage chaotic behavior, while the latter used an LSTM trained on a sample of random data to obtain indices into the digits of pi. Both papers reported a strong performance in statistical randomness tests. However, neither scheme sought to train an end-to-end" neural network PRNG, instead using the networks as components of more complex algorithms.
@cite_4 @cite_1 @cite_3 @cite_8
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Data-driven approaches employ the stochastic process that is simulated using random numbers. The random number can be thought of as an unpredictable value without bias or correlation, so it is essentially impossible to design a system that generates real' random numbers. There have been studies, therefore, that aimed at developing a pseudo random number generator, where the pseudo random number is not perfectly random, but it is practically useful. In this paper, we propose a new system for pseudo random number generation. The recurrent neural networks with long short-term memery (LSTM) units are used to mimic the appearance of a given sequence of irrational numbers (e.g., pi), and they are supposed to generate pseudo random numbers in iterative manner. We design an algorithm to make the output sequence to have no repetition or pattern, and prove its effectiveness by experimental results. This paper proposes a pseudo random number generator using Elman neural network. The proposed neural network is a recurrent neural network able to generate pseudo-random numbers from the weight matrices obtained from the layer weights of the Elman network. The proposed method is not computationally demanding and is easy to implement for varying bit sequences. The random numbers generated using our method have been subjected to frequency test and ENT test program. The results show that recurrent neural networks can be used as a pseudo random number generator(prng). Pseudo-random number generation on the Atari 2600 was commonly accomplished using a Linear Feedback Shift Register (LFSR). One drawback was that the initial seed for the LFSR had to be hard-coded into the ROM. To overcome this constraint, programmers sampled from the LFSR once per frame, including title and end screens. Since a human player will have some random amount of delay between seeing the title screen and starting to play, the LFSR state was effectively randomized at the beginning of the game despite the hard-coded seed. Other games used the player’s actions as a source of randomness. Notable pseudo-random games include Adventure in which a bat randomly steals and hides items around the game world and River Raid which used randomness to make enemy movements less predictable. Relying on the player to provide a source of randomness is not sufficient for computer controlled agents which are capable of memorizing and repeating pre-determined sequences of actions. Ideally, the games themselves would provide stochasticity generated from an external source such as the CPU clock. In practice, this was not an option presented by the hardware. Atari games are deterministic given a fixed policy leading to a set sequence of actions. This article discusses different approaches for adding stochasticity to Atari games and examines how effective each approach is at derailing an agent known to memorize action sequences. Additionally it is the authors’ hope that this article will spark discussion in the community over the following questions: Recently, neuron activations extracted from a pre-trained convolutional neural network (CNN) show promising performance in various visual tasks. However, due to the domain and task bias, using the features generated from the model pre-trained for image classification as image representations for instance retrieval is problematic. In this paper, we propose quartet-net learning to improve the discriminative power of CNN features for instance retrieval. The general idea is to map the features into a space where the image similarity can be better evaluated. Our network differs from the traditional Siamese-net in two ways. First, we adopt a double-margin contrastive loss with a dynamic margin tuning strategy to train the network which leads to more robust performance. Second, we introduce in the mimic learning regularization to improve the generalization ability of the network by preventing it from overfitting to the training data. Catering for the network learning, we collect a large-scale dataset, namely GeoPair, which consists of 68k matching image pairs and 63k non-matching pairs. Experiments on several standard instance retrieval datasets demonstrate the effectiveness of our method.