Integrasi Discrete Wavelet Transform dan Singular Value Decomposition pada Watermarking Citra untuk Perlindungan Hak Cipta

Tren masalah watermarking pada sekarang ini adalah bagaimana mengoptimalkan trade-off antara imperceptibility (visibilitas) citra ter-watermark terhadap pengaruh distorsi dan robustness terhadap penyisipan watermark. Masalah menggunakan kekuatan penyisipan berdasarkan Single Scaling Factor (SSF) atau Multiple Scaling Factor (MSF) juga ditemukan. Penelitian ini mengusulkan metode penyisipan watermark untuk perlindungan hak cipta pada citra dan algoritma ekstraksi citra ter-watermark yang dioptimalkan dengan penggabungan Discrete Wavelet Transform (DWT) dan Singular Value Decomposition (SVD). Nilai-nilai singular dari LL3 koefisien sub-band dari citra host dimodifikasi menggunakan nilai tunggal citra watermark biner menggunakan MSFs. Kontribusi utama dari skema yang diusulkan adalah aplikasi DWT-SVD untuk mengidentifikasi beberapa faktor skala yang optimal. Hasil penelitian menunjukkan bahwa skema yang diusulkan menghasilkan nilai Peak Signal to Noise Ratio (PSNR) yang tinggi, yang menunjukkan bahwa kualitas visual gambar yang baik pada masalah citra watermarking telah mengoptimalkan trade-off. Trade-off antara imperceptibility (visibilitas) citra ter-watermark terhadap pengaruh distorsi dan robustness citra ter-watermark terhadap operasi pengolahan citra. Nilai PSNR yang didapat pada citra yang diujikan: baboon=53,184; boat=53,328; cameraman=53,700; lena=53,668; man=53,328; dan pepper sebesar 52,662. Delapan perlakuan khusus pada hasil citra ter-watermark diujikan dan diekstraksi kembali yaitu JPEG 5%, Noise 5%, Gaussian filter 3x3, Sharpening, Histogram Equalization, Scaling 512-256, Gray Quantitation 1bit, dan Cropping 1/8. Hasil dari perlakuan khusus kemudian diukur nilai Normalized Cross-Correlation (NC) yang menghasilkan rata-rata semua citra diperoleh sebesar 0,999 dari satu. Hasil penelitian dari metode yang diusulkan lebih unggul nilai PSNR dan NC dari penelitian sebelumnya. Jadi dapat disimpulkan bahwa penerapan dengan metode DWT-SVD ini mampu menghasilkan citra yang robust namun memiliki tingkat imperceptibility yang cukup tinggi

Optimized DWT Based Digital Image Watermarking and Extraction Using RNN-LSTM

The rapid growth of Internet and the fast emergence of multi-media applications over the past decades have led to new problems such as illegal copying, digital plagiarism, distribution and use of copyrighted digital data. Watermarking digital data for copyright protection is a current need of the community. For embedding watermarks, robust algorithms in die media will resolve copyright infringements. Therefore, to enhance the robustness, optimization techniques and deep neural network concepts are utilized. In this paper, the optimized Discrete Wavelet Transform (DWT) is utilized for embedding the watermark. The optimization algorithm is a combination of Simulated Annealing (SA) and Tunicate Swarm Algorithm (TSA). After performing the embedding process, the extraction is processed by deep neural network concept of Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM). From the extraction process, the original image is obtained by this RNN-LSTM method. The experimental set up is carried out in the MATLAB platform. The performance metrics of PSNR, NC and SSIM are determined and compared with existing optimization and machine learning approaches. The results are achieved under various attacks to show the robustness of the proposed work

Intelligent watermarking of long streams of document images

Digital watermarking has numerous applications in the imaging domain, including (but not limited to) fingerprinting, authentication, tampering detection. Because of the trade-off between watermark robustness and image quality, the heuristic parameters associated with digital watermarking systems need to be optimized. A common strategy to tackle this optimization problem formulation of digital watermarking, known as intelligent watermarking (IW), is to employ evolutionary computing (EC) to optimize these parameters for each image, with a computational cost that is infeasible for practical applications. However, in industrial applications involving streams of document images, one can expect instances of problems to reappear over time. Therefore, computational cost can be saved by preserving the knowledge of previous optimization problems in a separate archive (memory) and employing that memory to speedup or even replace optimization for future similar problems. That is the basic principle behind the research presented in this thesis. Although similarity in the image space can lead to similarity in the problem space, there is no guarantee of that and for this reason, knowledge about the image space should not be employed whatsoever. Therefore, in this research, strategies to appropriately represent, compare, store and sample from problem instances are investigated. The objective behind these strategies is to allow for a comprehensive representation of a stream of optimization problems in a way to avoid re-optimization whenever a previously seen problem provides solutions as good as those that would be obtained by reoptimization, but at a fraction of its cost. Another objective is to provide IW systems with a predictive capability which allows replacing costly fitness evaluations with cheaper regression models whenever re-optimization cannot be avoided. To this end, IW of streams of document images is first formulated as the problem of optimizing a stream of recurring problems and a Dynamic Particle Swarm Optimization (DPSO) technique is proposed to tackle this problem. This technique is based on a two-tiered memory of static solutions. Memory solutions are re-evaluated for every new image and then, the re-evaluated fitness distribution is compared with stored fitness distribution as a mean of measuring the similarity between both problem instances (change detection). In simulations involving homogeneous streams of bi-tonal document images, the proposed approach resulted in a decrease of 95% in computational burden with little impact in watermarking performace. Optimization cost was severely decreased by replacing re-optimizations with recall to previously seen solutions. After that, the problem of representing the stream of optimization problems in a compact manner is addressed. With that, new optimization concepts can be incorporated into previously learned concepts in an incremental fashion. The proposed strategy to tackle this problem is based on Gaussian Mixture Models (GMM) representation, trained with parameter and fitness data of all intermediate (candidate) solutions of a given problem instance. GMM sampling replaces selection of individual memory solutions during change detection. Simulation results demonstrate that such memory of GMMs is more adaptive and can thus, better tackle the optimization of embedding parameters for heterogeneous streams of document images when compared to the approach based on memory of static solutions. Finally, the knowledge provided by the memory of GMMs is employed as a manner of decreasing the computational cost of re-optimization. To this end, GMM is employed in regression mode during re-optimization, replacing part of the costly fitness evaluations in a strategy known as surrogate-based optimization. Optimization is split in two levels, where the first one relies primarily on regression while the second one relies primarily on exact fitness values and provide a safeguard to the whole system. Simulation results demonstrate that the use of surrogates allows for better adaptation in situations involving significant variations in problem representation as when the set of attacks employed in the fitness function changes. In general lines, the intelligent watermarking system proposed in this thesis is well adapted for the optimization of streams of recurring optimization problems. The quality of the resulting solutions for both, homogeneous and heterogeneous image streams is comparable to that obtained through full optimization but for a fraction of its computational cost. More specifically, the number of fitness evaluations is 97% smaller than that of full optimization for homogeneous streams and 95% for highly heterogeneous streams of document images. The proposed method is general and can be easily adapted to other applications involving streams of recurring problems

Steganography Approach to Image Authentication Using Pulse Coupled Neural Network

This paper introduces a model for the authentication of large-scale images. The crucial element of the proposed model is the optimized Pulse Coupled Neural Network. This neural network generates position matrices based on which the embedding of authentication data into cover images is applied. Emphasis is placed on the minimalization of the stego image entropy change. Stego image entropy is consequently compared with the reference entropy of the cover image. The security of the suggested solution is granted by the neural network weights initialized with a steganographic key and by the encryption of accompanying steganographic data using the AES-256 algorithm. The integrity of the images is verified through the SHA-256 hash function. The integration of the accompanying and authentication data directly into the stego image and the authentication of the large images are the main contributions of the work