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The rapid growth in Internet and communication technology has facilitated an escalation in the exchange of digital multimedia content. This has resulted in an increase in copyright infringement, which has led to a greater demand for more robust copyright protection mechanisms. Digital watermarking is a means of detecting ownership and illegal use of digital products. This paper presents an approach to watermarking images by embedding QR code information in a digital image.Abstract. The rapid growth in Internet and communication technology has facilitated an escalation in the exchange of digital multimedia content. This has resulted in an increase in copyright infringement, which has led to a greater demand for more robust copyright protection mechanisms. Digital watermarking is a means of detecting ownership and illegal use of digital products. This paper presents an approach to watermarking images by embedding QR code information in a digital image. The notion of the proposed scheme is to capitalize on the error correction mechanism that is inherent in the QR code structure, in order to increase the robustness of the watermark. By employing the QR code's error correction mechanism, watermark information contained within a watermarked image can potentially be decoded even if the image has been altered or distorted by an adversary. This paper studies the characteristics of the proposed scheme and presents experiment results examining the robustness and security of the QR code watermarking approach.

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With the increasing threat of cyber attacks, machine learning techniques have been researched extensively in the area of network intrusion detection. Such techniques can potentially provide a means for the real-time automated detection of attacks and abnormal traffic patterns. However, misclassification is a common problem in machine learning techniques for intrusion detection, and a lack of insight into why such misclassification occurs impedes the improvement of machine learning models. This paper presents an approach to visualizing network intrusion detection data in 3D. The purpose of this is to facilitate the understanding of network intrusion detection datasets using a visual representation to reflect the geometric relationship between various categories of network traffic. This can potentially provide useful insight to aid the design of machine learning techniques. This paper demonstrates the usefulness of the proposed 3D visualization approach by presenting results of experiments on commonly used network intrusion detection datasets.

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