CNN Transfer Learning for Automatic Fruit Recognition for Future Class of Fruit

Deep fruit recognition model learned on big dataset outperform fruit recognition task on difficult unconstrained fruit dataset. But in practice, we are often lack of resources to learn such a complex model, or we only have very limited training samples for a specific fruit recognition task. In this study we address the problem of adding new classes to an existing deep convolutional neural network framework. We extended our prior work for automatic fruit recognition by applying transfer learning techniques to adding new classes to existing model which was trained for 15 different kind of fruits. Pre-trained model was previously trained on a large-scale dataset of 44406 images. To add new class of fruit in our pre-trained model, we need to train a new classifier which will be trained for scratch, on the top of pre-trained model so, that we can re- purpose the feature learned previously for the dataset. Transfer learning using our pre-trained model has been demonstrated to give the best classification accuracy of 95.00%. The experimental results demonstrate that our proposed CNN framework is superior to the previous state-of-the- art networks

CNN Based Adversarial Embedding with Minimum Alteration for Image Steganography

Historically, steganographic schemes were designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML) based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers. However, simply applying perturbations on stego images as adversarial examples may lead to the failure of data extraction and introduce unexpected artefacts detectable by other classifiers. In this paper, we present a steganographic scheme with a novel operation called adversarial embedding, which achieves the goal of hiding a stego message while at the same time fooling a convolutional neural network (CNN) based steganalyzer. The proposed method works under the conventional framework of distortion minimization. Adversarial embedding is achieved by adjusting the costs of image element modifications according to the gradients backpropagated from the CNN classifier targeted by the attack. Therefore, modification direction has a higher probability to be the same as the sign of the gradient. In this way, the so called adversarial stego images are generated. Experiments demonstrate that the proposed steganographic scheme is secure against the targeted adversary-unaware steganalyzer. In addition, it deteriorates the performance of other adversary-aware steganalyzers opening the way to a new class of modern steganographic schemes capable to overcome powerful CNN-based steganalysis.Comment: Submitted to IEEE Transactions on Information Forensics and Securit

FeatureTransfer: Unsupervised Domain Adaptation for Cross-Domain Deepfake Detection

Recently, various Deepfake detection methods have been proposed, and most of them are based on convolutional neural networks (CNNs). These detection methods suffer from overfitting on the source dataset and do not perform well on cross-domain datasets which have different distributions from the source dataset. To address these limitations, a new method named FeatureTransfer is proposed in this paper, which is a two-stage Deepfake detection method combining with transfer learning. Firstly, The CNN model pretrained on a third-party large-scale Deepfake dataset can be used to extract the more transferable feature vectors of Deepfake videos in the source and target domains. Secondly, these feature vectors are fed into the domain-adversarial neural network based on backpropagation (BP-DANN) for unsupervised domain adaptive training, where the videos in the source domain have real or fake labels, while the videos in the target domain are unlabelled. The experimental results indicate that the proposed method FeatureTransfer can effectively solve the overfitting problem in Deepfake detection and greatly improve the performance of cross-dataset evaluation

Impact of H2O on organic-inorganic hybrid perovskite solar cells

The performance and stability of organic-inorganic hybrid perovskite solar cells (PSCs) is sensitive to water and moisture in an ambient environment. Understanding how H2O influences the perovskite material is also important for developing appropriate control strategies to mitigate the problem. Here we provide a comprehensive review on the effect of water on the state-of-the-art lead-based perovskite solar cells in terms of perovskite material design, perovskite film preparation, device fabrication, and photovoltaic application. It is found that a moderate amount of water can facilitate nucleation and crystallization of the perovskite material, resulting in better perovskite film quality and enhanced PSC performance. The perovskite materials are irreversibly destroyed by H2O after a certain level of water, but they exihibit better tolerance than initially expected. Humidity resistant fabrication of high-performance PSC devices and modules should therefore be favoured. Generally, water shows a negative effect on the long-term stability and lifetime of PSCs. To reduce the effects from water during outdoor operation, attention should be paid to different protection methods such as varying the perovskite composition, optimizing the electron/hole transport layer and encapsulation of the device.Peer reviewe