A Detection Approach Using LSTM-CNN for Object Removal Caused by Exemplar-Based Image Inpainting

Exemplar-based image inpainting technology is a “double-edged sword”. It can not only restore the integrity of image by inpainting damaged or removed regions, but can also tamper with the image by using the pixels around the object region to fill in the gaps left by object removal. Through the research and analysis, it is found that the existing exemplar-based image inpainting forensics methods generally have the following disadvantages: the abnormal similar patches are time-consuming and inaccurate to search, have a high false alarm rate and a lack of robustness to multiple post-processing combined operations. In view of the above shortcomings, a detection method based on long short-term memory (LSTM)-convolutional neural network (CNN) for image object removal is proposed. In this method, CNN is used to search for abnormal similar patches. Because of CNN’s strong learning ability, it improves the speed and accuracy of the search. The LSTM network is used to eliminate the influence of false alarm patches on detection results and reduce the false alarm rate. A filtering module is designed to eliminate the attack of post-processing operation. Experimental results show that the method has a high accuracy, and can resist the attack of post-processing combination operations. It can achieve a better performance than the state-of-the-art approaches

CFSH: Factorizing sequential and historical purchase data for basket recommendation.

To predict what products customers will buy in next transaction is an important task. Existing work in next-basket prediction can be summarized into two paradigms. One is the item-centric paradigm, where sequential patterns are mined from customers' transactional data and leveraged for prediction. However, these approaches usually suffer from the data sparseness problem. The other is the user-centric paradigm, where collaborative filtering techniques have been applied on customers' historical data. However, these methods ignore the sequential behaviors of customers which is often crucial for next-basket prediction. In this paper, we introduce a hybrid method, namely the Co-Factorization model over Sequential and Historical purchase data (CFSH for short) for next-basket recommendation. Compared with existing methods, our approach conveys the following merits: 1) By mining global sequential patterns, we can avoid the sparseness problem in traditional item-centric methods; 2) By factorizing product-product and customer-product matrices simultaneously, we can fully exploit both sequential and historical behaviors to learn customer and product representations better; 3) By using a hybrid recommendation method, we can achieve better performance in next-basket prediction. Experimental results on three real-world purchase datasets demonstrated the effectiveness of our approach as compared with the state-of-the-art methods

An Intelligent Forensics Approach for Detecting Patch-Based Image Inpainting

Image inpainting algorithms have a wide range of applications, which can be used for object removal in digital images. With the development of semantic level image inpainting technology, this brings great challenges to blind image forensics. In this case, many conventional methods have been proposed which have disadvantages such as high time complexity and low robustness to postprocessing operations. Therefore, this paper proposes a mask regional convolutional neural network (Mask R-CNN) approach for patch-based inpainting detection. According to the current research, many deep learning methods have shown the capacity for segmentation tasks when labeled datasets are available, so we apply a deep neural network to the domain of inpainting forensics. This deep learning model can distinguish and obtain different features between the inpainted and noninpainted regions. To reduce the missed detection areas and improve detection accuracy, we also adjust the sizes of the anchor scales due to the inpainting images and replace the original nonmaximum suppression single threshold with an improved nonmaximum suppression (NMS). The experimental results demonstrate this intelligent method has better detection performance over recent approaches of image inpainting forensics

Weakly Supervised GAN for Image-to-Image Translation in the Wild

Generative Adversarial Networks (GANs) have achieved significant success in unsupervised image-to-image translation between given categories (e.g., zebras to horses). Previous GANs models assume that the shared latent space between different categories will be captured from the given categories. Unfortunately, besides the well-designed datasets from given categories, many examples come from different wild categories (e.g., cats to dogs) holding special shapes and sizes (short for adversarial examples), so the shared latent space is troublesome to capture, and it will cause the collapse of these models. For this problem, we assume the shared latent space can be classified as global and local and design a weakly supervised Similar GANs (Sim-GAN) to capture the local shared latent space rather than the global shared latent space. For the well-designed datasets, the local shared latent space is close to the global shared latent space. For the wild datasets, we will get the local shared latent space to stop the model from collapse. Experiments on four public datasets show that our model significantly outperforms state-of-the-art baseline methods

An Improved Permission Management Scheme of Android Application Based on Machine Learning

The Android permission mechanism prevents malicious application from accessing the mobile multimedia data and invoking the sensitive API. However, there are still lots of deficiencies in the current permission management, which results in the permission mechanism being unable to protect users’ private data properly. In this paper, a dynamic management scheme of Android permission based on machine learning is proposed to solve the problem of the existing permission mechanism. In order to accomplish the dynamic management, the proposed scheme maintains a dynamic permission management database which records the state of permissions for each application. Only the permission which is granted state in the database can be used in this application. In the whole process, the scheme first classifies the application by means of machine learning, then retrieves the corresponding permission information from databases, and issues the dangerous permission warning to users. Finally, the scheme updates the dynamic management database according to the users’ decisions. Through this scheme, users can prevent malicious behaviour of accessing private data and invoking sensitive API in time. The solution increases the flexibility of permission management and improves the security and reliability of multimedia data in Android devices

Layerwise Adversarial Learning for Image Steganography

Image steganography is a subfield of pattern recognition. It involves hiding secret data in a cover image and extracting the secret data from the stego image (described as a container image) when needed. Existing image steganography methods based on Deep Neural Networks (DNN) usually have a strong embedding capacity, but the appearance of container images is easily altered by visual watermarks of secret data. One of the reasons for this is that, during the end-to-end training process of their Hiding Network, the location information of the visual watermarks has changed. In this paper, we proposed a layerwise adversarial training method to solve the constraint. Specifically, unlike other methods, we added a single-layer subnetwork and a discriminator behind each layer to capture their representational power. The representational power serves two purposes: first, it can update the weights of each layer which alleviates memory requirements; second, it can update the weights of the same discriminator which guarantees that the location information of the visual watermarks remains unchanged. Experiments on two datasets show that the proposed method significantly outperforms the most advanced methods

Facial Pose and Expression Transfer Based on Classification Features

Transferring facial pose and expression features from one face to another is a challenging problem and an interesting topic in pattern recognition, but is one of great importance with many applications. However, existing models usually learn to transfer pose and expression features with classification labels, which cannot hold all the differences in shape and size between conditional faces and source faces. To solve this problem, we propose a generative adversarial network model based on classification features for facial pose and facial expression transfer. We constructed a two-stage classifier to capture the high-dimensional classification features for each face first. Then, the proposed generation model attempts to transfer pose and expression features with classification features. In addition, we successfully combined two cost functions with different convergence speeds to learn pose and expression features. Compared to state-of-the-art models, the proposed model achieved leading scores for facial pose and expression transfer on two datasets

design and implementation of earth operating system

利用箱式电阻炉、SEM等手段,研究了一种定向凝固Co基合金在不同介质中的热腐蚀行为及机理。研究结果表明:合金在Na2SO4中的热腐蚀最为严重,在75%Na2SO4+25%NaCl中的热腐蚀程度最轻。合金在Na2SO4中的热腐蚀过程中主要发生了酸性熔融反应,形成了Al2(SO4)3,Cr2Ni3,Cr4Ni15W和Al4CrNi15等腐蚀产物,而在NaCl中的热腐蚀主要发生了活性氧化反应,合金在75%Na2SO4+25%NaCl中热腐蚀时,部分酸性熔融反应和活性氧化反应受到抑制,主要形成了一系列的Ni-S化合物。National High Technology Research and Development Program of China (863 Program) 2007AA010601; National Natural Science Foundation of China 61070207; LOIS SYSKF1006As secure infrastructural software, secure operating systems are well known for its protection against kinds of threats and attacks. In this paper, we report the work of building EARTH operating system towards to "structurized protection" level of national security standards. EARTH has a flexible architecture supporting dynamic multiple policies, effective mandatory access control mechanism. We give formal system specification and proof of the security models and provide covert channel analysis and mitigation methods. Our experiments show that EARTH has good system performance. Furthermore, the research and development experiences and lessons learned in EARTH project is discussed

A Visually Secure Image Encryption Based on the Fractional Lorenz System and Compressive Sensing

Recently, generating visually secure cipher images by compressive sensing (CS) techniques has drawn much attention among researchers. However, most of these algorithms generate cipher images based on direct bit substitution and the underlying relationship between the hidden and modified data is not considered, which reduces the visual security of cipher images. In addition, performing CS on plain images directly is inefficient, and CS decryption quality is not high enough. Thus, we design a novel cryptosystem by introducing vector quantization (VQ) into CS-based encryption based on a 3D fractional Lorenz chaotic system. In our work, CS compresses only the sparser error matrix generated from the plain and VQ images in the secret generation phase, which improves CS compression performance and the quality of decrypted images. In addition, a smooth function is used in the embedding phase to find the underlying relationship and determine relatively suitable modifiable values for the carrier image. All the secret streams are produced by updating the initial values and control parameters from the fractional chaotic system, and then utilized in CS, diffusion, and embedding. Simulation results demonstrate the effectiveness of the proposed method