Biblio

The prevalence of photo processing apps suggests the demands of picture editing. As an implementation of the convolutional neural network, style transfer has been deep investigated and there are supported materials to realize it on PC platform. However, few approaches are mentioned to deploy a style transfer model on the mobile and meet the requirements of mobile users. The traditional style transfer model takes hours to proceed, therefore, based on a Perceptual Losses algorithm [1], we created a feedforward neural network for each style and the proceeding time was reduced to a few seconds. The training data were generated from a pre-trained convolutional neural network model, VGG-19. The algorithm took thousandth time and generated similar output as the original. Furthermore, we optimized the model and deployed the model with TensorFlow Mobile library. We froze the model and adopted a bitmap to scale the inputs to 720×720 and reverted back to the original resolution. The reverting process may create some blur but it can be regarded as a feature of art. The generated images have reliable quality and the waiting time is independent of the content and pattern of input images. The main factor that influences the proceeding time is the input resolution. The average waiting time of our model on the mobile phone, HUAWEI P20 Pro, is less than 2 seconds for 720p images and around 2.8 seconds for 1080p images, which are ten times slower than that on the PC GPU, Tesla T40. The performance difference depends on the architecture of the model.

The purpose of this paper is to improve the safety of chaotic image encryption algorithm. Firstly, to achieve this goal, it put forward two improved chaotic system logistic and henon, which covered an promoted henon chaotic system with better probability density, and an 2-dimension logistic chaotic system with high Lyapunov exponents. Secondly, the chaotic key stream was generated by the new 2D logistic chaotic system and optimized henon mapping, which mixed in dynamic proportions. The conducted sequence has better randomness and higher safety for image cryptosystem. Thirdly, we proposed algorithm takes advantage of the compounded chaotic system Simulation experiment results and security analysis showed that the proposed scheme was more effective and secure. It can resist various typical attacks, has high security, satisfies the requirements of image encryption theoretical.

In this paper we present a new approach, named DLGraph, for malware detection using deep learning and graph embedding. DLGraph employs two stacked denoising autoencoders (SDAs) for representation learning, taking into consideration computer programs' function-call graphs and Windows application programming interface (API) calls. Given a program, we first use a graph embedding technique that maps the program's function-call graph to a vector in a low-dimensional feature space. One SDA in our deep learning model is used to learn a latent representation of the embedded vector of the function-call graph. The other SDA in our model is used to learn a latent representation of the given program's Windows API calls. The two learned latent representations are then merged to form a combined feature vector. Finally, we use softmax regression to classify the combined feature vector for predicting whether the given program is malware or not. Experimental results based on different datasets demonstrate the effectiveness of the proposed approach and its superiority over a related method.

In this paper, we propose a CPA-Secure encryption scheme with equality test. Unlike other public key solutions, in our scheme, only the data owner can encrypt the message and get the comparable ciphertext, and only the tester with token who can perform the equality test. Our encryption scheme is based on multiplicative homomorphism of ElGamal Encryption and Non Interactive Zero Knowledge proof of Discrete Log. We proof that the proposed scheme is OW-CPA security under the attack of the adversary who has equality test token, and IND-CPA security under the attack of adversary who can not test the equality. The proposed scheme only suppose to compare two ciphertexts encrypted by same user, though it is less of flexibility, it is efficient and more suitable for data outsourcing scenario.

We present a framework for learning to describe finegrained visual differences between instances using attribute phrases. Attribute phrases capture distinguishing aspects of an object (e.g., “propeller on the nose” or “door near the wing” for airplanes) in a compositional manner. Instances within a category can be described by a set of these phrases and collectively they span the space of semantic attributes for a category. We collect a large dataset of such phrases by asking annotators to describe several visual differences between a pair of instances within a category. We then learn to describe and ground these phrases to images in the context of a reference game between a speaker and a listener. The goal of a speaker is to describe attributes of an image that allows the listener to correctly identify it within a pair. Data collected in a pairwise manner improves the ability of the speaker to generate, and the ability of the listener to interpret visual descriptions. Moreover, due to the compositionality of attribute phrases, the trained listeners can interpret descriptions not seen during training for image retrieval, and the speakers can generate attribute-based explanations for differences between previously unseen categories. We also show that embedding an image into the semantic space of attribute phrases derived from listeners offers 20% improvement in accuracy over existing attributebased representations on the FGVC-aircraft dataset.