Data Mining - Concepts and Techniques.

Deep learning technologies, which are the key components of state-of-the-art Artificial Intelligence (AI) services, have shown great success in providing human-level capabilities for a variety of tasks, such as visual analysis, speech recognition, and natural language processing and etc. Building a production-level deep learning model is a non-trivial task, which requires a large amount of training data, powerful computing resources, and human expertises. Therefore, illegitimate reproducing, distribution, and the derivation of proprietary deep learning models can lead to copyright infringement and economic harm to model creators. Therefore, it is essential to devise a technique to protect the intellectual property of deep learning models and enable external verification of the model ownership. In this paper, we generalize the "digital watermarking'' concept from multimedia ownership verification to deep neural network (DNNs) models. We investigate three DNN-applicable watermark generation algorithms, propose a watermark implanting approach to infuse watermark into deep learning models, and design a remote verification mechanism to determine the model ownership. By extending the intrinsic generalization and memorization capabilities of deep neural networks, we enable the models to learn specially crafted watermarks at training and activate with pre-specified predictions when observing the watermark patterns at inference. We evaluate our approach with two image recognition benchmark datasets. Our framework accurately (100%) and quickly verifies the ownership of all the remotely deployed deep learning models without affecting the model accuracy for normal input data. In addition, the embedded watermarks in DNN models are robust and resilient to different counter-watermark mechanisms, such as fine-tuning, parameter pruning, and model inversion attacks.

Protecting Intellectual Property of Deep Neural Networks with Watermarking

In this paper, a novel robust color image watermarking method based on Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is proposed. In this method, RGB cover image is divided into red, green and blue components. DCT and DWT are applied to each color components. Grayscale watermark image is scrambled by using Arnold transform. DCT is performed to the scrambled watermark image. Transformed watermark image is then divided into equal smaller parts. DCT coefficients of each watermark parts are embedded into four DWT bands of the color components of the cover image. The robustness of the proposed color image watermarking has been demonstrated by applying various image processing operations such as rotating, resizing, filtering, jpeg compression, and noise adding to the watermarked images. Experimental results show that the proposed method is robust to the linear and nonlinear attacks and the transparency of the watermarked images has been protected.

A novel hybrid DCT and DWT based robust watermarking algorithm for color images

A container is a gathering of neurons whose action vector speaks to the instantiation parameters of a particular kind of substance, for example, an item or an article part. We utilize the length of the action vector to speak to the likelihood that the substance exists and its introduction to speak to the instantiation parameters. Compelling cases at one dimension make expectations, utilizing change networks, for the instantiation parameters of more elevated amount containers. At the point when different forecasts concur, a higher amount of container ends up dynamic. We demonstrate that a discriminatively prepared, multilayer case framework accomplishes best in class execution on Modified National Institute of Standards and Technology (MNIST) and is extensively superior to a deep learning algorithm at perceiving exceedingly covering digits. Deep learning algorithms encouraged by the function and structure of the brain. The deep extreme learning machine (DELM) approach is used to construct a compound that has the least error and highest reliability. All layers are jointly or greedily optimized, depending on the strategy. Deep extreme learning learns all the layers. This paper shows research on the expectation of the MNIST dataset using a DELM. In this article to predict digits better, we have used feedforward and backward propagation deep learning neural networks. When the results were considered, it was observed that deep extreme learning neural network has the highest accuracy rate with 70% of training (42,000 samples), 30% of test and validation (28,000 examples). When comparing the results, it was seen that the intelligent routing between capsules empowered with DELM (IRBC DELM) has the highest precision rate of 97.8%. Simulation results validate the prediction effectiveness of the proposed DELM strategy.

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Intelligent routing between capsules empowered with deep extreme machine learning technique

Wavelet transform based on Meyer algorithm for image edge and blocking artifact reduction

In this paper, we suggested a new secured digital watermarking technique that can be used for the data validation. The suggested method is secure and efficient. The secured digital watermark is added by the hybrid method for which we have used combination of discrete cosine transform (DCT) and discrete wavelet transform (DWT) methods along with cryptographic technique (Arnold Transform). This technique provides strong robustness and perception transparency to the watermarked image and original image against different kind of attacks like cropping, noise and scaling. We have also compared these results with Least Significant Bit and Discrete Cosine methods.

Secured digital image watermarking with discrete cosine transform and discrete wavelet transform method

Pattern recognition deals with categorization of input data into one of the given classes based on extraction of features. Handwritten Character Recognition (HCR) is one of the well-known applications of pattern recognition. For any recognition system, an important part is feature extraction. A proper feature extraction method can increase the recognition ratio. In this paper, a chain code based feature extraction method is investigated for developing HCR system. Chain code is working based on 4-neighborhood or 8- neighborhood methods. In this paper, 8-neighborhood method has been implemented which allows generation of eight different codes for each character. These codes have been used as features of the character image, which have been later on used for training and testing for Neural Network (NN) and Support Vector Machine (SVM) classifiers. In this work we have also implemented HCR system with the use of correlation coefficient. Comparison of all the methods for HCR systems are highlighted at the end.

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Handwritten Character Recognition System using Chain code and Correlation Coefficient

In recent year a lot of web applications have been released in the world. At the same time, Zero-Day attacks against web application vulnerabilities have also increased. In such a scenario, it is necessary to make web applications more secure. However checking all web vulnerabilities by manually is very difficult and time-consuming. Therefore, we need a web application vulnerability scanner which is used for detecting security vulnerabilities in web services by underlining the service from the attacker's point of view. Web Application Penetration Testing (WAPT) plays an important role in every modern organization but, if an organization web apps does not properly test and secure then adversaries can compromise your applications, steal organization data and. damage business performance. Unfortunately, many organizations are under an illusion that a web application scanner which they are using will sincerely discover loopholes in their systems. According to research and study, there are different penetration testing tools have provide different performance on vulnerabilities detection. In this paper we have analyze and take survey of the different Zero-day vulnerability, how we can help organizations in testing their web applications in order to build reliable and secure applications.

A Review on 0-day Vulnerability Testing in Web Application

Digital video is very important in day to day life. It is important in multimedia data for creating, processing, transmitting and storing digital information in many forms like image, audio, and video. The invention of video editing technology, video are used in a wide spread. Digital video is useful in education, medical treatment, and various another field. Unauthorized alteration or modification is done by an attacker to maliciously forge a video sequence for video forensic e.g. Frames are repeated, cropping the frame, copying the frame, deleting the frame. In this paper present review of several video forgery detection methods, those are used to find whether the video is real or fake and video authentication technique.

Digital Video Forgery Detection and Authentication Technique - A Review

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Ravi Sheth

Papers

Secured digital image watermarking with discrete cosine transform and discrete wavelet transform method

Handwritten Character Recognition System using Chain code and Correlation Coefficient

A Review on 0-day Vulnerability Testing in Web Application

Digital Video Forgery Detection and Authentication Technique - A Review

Secured Digital Video Authentication System