Manipulated images and videos have become increasingly realistic due to the tremendous progress of deep convolutional neural networks (CNNs). While technically intriguing, such progress raises a number of social concerns related to the advent and spread of fake information and fake news. Such concerns necessitate the introduction of robust and reliable methods for fake image and video detection. Toward this in this work, we study the ability of state-of-the-art video CNNs including 3D ResNet, 3D ResNeXt, and I3D in detecting manipulated videos. In addition, and toward a more robust detection, we investigate the effectiveness of attention mechanisms in this context. Such mechanisms are introduced in CNN architectures in order to ensure that robust features are being learnt. We test two attention mechanisms, namely SE-block and Non-local networks. We present related experimental results on videos tampered by four manipulation techniques, as included in the FaceForensics++ dataset. We investigate three scenarios, where the networks are trained to detect (a) all manipulated videos, (b) each manipulation technique individually, as well as (c) the veracity of videos pertaining to manipulation techniques not included in the train set.

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In this study, an Intrusion Detection System (IDS) is designed based on Machine Learning classifiers, and its performance is evaluated for the set of attacks entailed in the UNSW-NB15 dataset. UNSW-NB15 dataset contains 2,540,226 realistic network data instances and 49 features. Most research uses a representative sample of this dataset with present training and testing subsets, which includes 257,673 records in total. The dataset was submitted to visual data analysis to discover potential reasons or flaws which likely challenge Machine Learning classifiers. Pre-processing strategies are necessary before this data can be used for data-driven prototype development for IDS because of the class representation imbalance with pattern counts and feature overlap. The method used for pre-processing is implemented by min-max scaling in the normalization phase, followed by applying Elastic Net and Sequential Feature Selection (SFS) algorithms. This work employed ensemble methods using three base classifiers, namely Balanced Bagging, XGBoost, and RF-HDDT, augmented to address the imbalance issue. Parameters of Balanced Bagging and XGBoost are tuned for the imbalanced data, and the Hellinger distance metric supplements random Forest to address the limitations of the default distance metric. Two new algorithms are proposed to address the class overlap issue in the dataset and applied during training. These two algorithms are leveraged to help improve the performance on the testing dataset by affecting the final classification decision made by three base classifiers as part of the ensemble classifier, which employs a majority vote combiner. The performance evaluation of the proposed method for binary and multi-category classification was evaluated using standard metrics, including those generated from the confusion matrix, and compared to other studies using the same dataset. The proposed design outperforms those reported in the literature by a significant margin for binary and multi-category classification cases.

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The stock market occurs from the interaction of a group of buyers (investors) and sellers of shares (companies), who represent ownership of the business. This includes a security listed on a public stock exchange under government supervision. Shares or stock market can be classified according to the country where the company is domiciled, for example Gudang Garam (company in Indonesia) which is domiciled in Indonesia and traded on the Indonesia Stock Exchange. The stock market has become an attractive and profitable investment today for investors and the stock market has grown rapidly over the years and is getting more and more attention because it deals with the future of money. However, a lot of investors are still worried to invest in stock market today, even investing in stock market results a huge profit. This reason can be the volatility in stock market. Therefore, this study focused on the investors’ perceptions towards stock market in different geographical areas. The data collected through online interview and distributing questionnaires to respondents in order to understand their behaviors, attitudes, desires, perspectives and level of awareness towards the stock market. The results showed that investors’ perceptions on buying shares in Asia are represented by several indicators, such as neutral information, accounting information, and social relevance, in which these three indicators generate impressions of the company’s activities based on profits and fundamental thinking patterns. Therefore, this will have an influence on investors in making decisions on the shares which will be chosen by them in the future.