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Ditemukan 26752 dokumen yang sesuai dengan query
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Andika Candra Jaya
"ABSTRAK
Steganografi adalah metode pengamanan informasi yang dikembangkan untuk menutupi keberadaan informasi itu alih-alih mengacak informasi tersebut. Permasalahan dari steganografi adalah kapasitas yang terbatas karena keharusan steganografi untuk tidak menimbulkan kecurigaan akibat derau pada medium. Tesis ini membahas teknik yang dapat meningkatkan kapasitas steganografi dengan memanipulasi gambar sedemikian sehingga kapasitas steganografi dapat ditingkatkan dengan teknik-teknik manipulasi yang diijinkan dalam kompetisi fotografi. Teknik manipulasi gambar yang dapat menunjukkan peningkatan kapasitas steganografi gambar antara lain, peningkatan kontras, pengaturan kecerahan, peningkatan kualitas, perubahan dimensi terbatas, dan pemilihan gambar dengan banyak subyek.

ABSTRACT
Steganography is a way to secure information by hiding it as opposed to scrambling said information as in cryptography. The problem with steganography is that it often limited in capacity due to requirement to not raise suspicion regarding the image distortion. This thesis discussed techniques that enhance capacity using image manipulation techniques allowed in photography contests. Capacity-enhancing techniques are equalization, contrast stretching, brightness adjustment, quality improvement, limited image scaling, and choosing images with more subjects"
2018
T54462
UI - Tesis Membership  Universitas Indonesia Library
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Zulfany Nurluthfia
"Pada era digital ini, bertambahnya tingkat kejahatan dunia maya, seperti pencurian, pemalsuan, dan penyalahgunaan informasi yang sifatnya rahasia, telah memicu berkembangnya teknik pengamanan informasi. Dua teknik yang dapat digunakan untuk mengamankan informasi adalah kriptografi dan steganografi.
Tujuan dari skripsi ini adalah menyusun algoritma penyembunyian informasi berupa citra digital dengan menggabungkan dua teknik, yaitu teknik enkripsi menggunakan keystream yang dibangkitkan oleh fungsi logistic map dan teknik penyisipan Least Significant Bit (LSB) berpola 3-3-2.
Dari hasil pengujian dan analisis, ditemukan bahwa algoritma ini memiliki ruang kunci sebesar 1030, sensitivitas kunci hingga 10−16, keystream yang dihasilkan terbukti acak berdasarkan frequency (monobit) test, distribusi nilai pixel-pixel dari citra terenkripsinya adalah uniform, dan nilai PSNR antara cover object dengan stego object di kisaran 47.123 − 57.586 yang mana mengindikasikan bahwa stego object yang dihasilkan memiliki kualitas imperceptibility yang baik.

In this digital era, the increasing number of cyber crime, such as theft, forgery, and abuse of secret information, has triggered the development of information security techniques. Two techniques that could be used to secure information are cryptography and steganography.
The purpose of this bachelor thesis is to design an algorithm to hide information in image form using the combination of two techniques, encryption technique with keystream generated by logistic map function and Least Significant Bit (LSB) with 3-3-2 pattern to embed information.
According to various tests and analysis, it is discovered that this algorithm has key space of 1030, key sensitivity up to 10−16, keystream that is proved to be random by frequency (monobit) test, pixel value distribution of encrypted image is uniform, and PSNR between cover object and stego object is in range 47.123 − 57.586 which indicates that the produced stego object has good imperceptibility quality.
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Depok: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2016
S62152
UI - Skripsi Membership  Universitas Indonesia Library
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Yuliana
"
ABSTRAK
Along with the increasing use of the internet in Indonesia, the threat to messages in image media has increased. The confidential data that will be sent requires security so that it can be read by the recipient of the message. For this reason, it is necessary to design a security system for this picture's media message by encrypting and decrypting it then then hiding the message. The algorithm used by combining various cryptographic algorithms and steganography techniques using the LSB method. The test results show the security of the message on the image media, especially in protecting the copyright rights of the image. The original original image measuring 242 kb in the .jpg format will increase in value after adding secret data with a size of 536 kb using the .png format. This system successfully displays secret messages in the image and does not change the cover of the image."
Yogyakarta: Pusat Penelitian dan Pengabdian Pada Masyarakat (P3M) STTA, 2019
600 JIA XI:2 (2019)
Artikel Jurnal  Universitas Indonesia Library
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Akbar Delmi
"Steganografi dan Kriptografi merupakan metode yang digunakan untuk mengamankan suatu informasi. Kriptografi yang digunakan memakai prinsip Chaos dengan menggunakan fungsi Chaos Arnold Cat Map untuk menjamin keacakan informasi. Sedangkan pada steganografi, digunakan metode penyisipan Least Significant Bit Matching Revisited (LSBMR). Wilayah penyisipan pesan berada di edge citra digital untuk menjamin pesan tidak terdeteksi pada citra secara visual.
Metode yang digunakan untuk mendeteksi wilayah edge yaitu dengan menggunakan Canny edge Detection. Hasil uji secara kualitatif dan kuantitatif dengan menggunakan Peak Signal to Ratio (PSNR), didapatkan nilai hasil yaitu 72-44 dB untuk data pesan dengan ukuran 10% dari media yang disisipkannya.

Steganography and Cryptography is the method used to secure the information. Cryptography is used the principle of a Chaos by using Cat Arnold Map function to assure randomness. While in steganography, the method is used Least Significant Bit Matching Revisited. Embedding region were on edge digital imagery to ensure the message was not detected in the image by visual.
The method used to detect the edge region by using Canny edge Detection. The test results obtained by Peak Signal to Ratio (PSNR) is 72-44 dB for data messages with a size of 10% of the media cover.
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Depok: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2016
S64769
UI - Skripsi Membership  Universitas Indonesia Library
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Novita Angraini
"Advanced Encryption Standard (AES) adalah suatu standar algoritma block cipher yang digunakan sebagai penerapan dari kriptografi. Perkembangan serangan pada algoritma AES mendorong banyaknya penelitian terkait modifikasi pada algoritma AES dengan tujuan untuk meningkatkan keamanan pada algoritma tersebut serta untuk menghasilkan alternatif dari algoritma enkripsi yang dapat digunakan untuk mengamankan data. Pada penelitian ini, telah dilakukan modifikasi terhadap algoritma AES dengan mengganti S-box menggunakan perfect SAC S-box pada proses SubBytes dan menggunakan matriks MDS involutary yang merupakan matriks M0 Clefia pada proses Mixcolumn. Perfect SAC S-box memiliki nilai rata-rata SAC yang tepat 0,5. Berdasarkan hasil pengujian didapatkan bahwa perfect SAC S-box memiliki hasil uji SAC yang lebih baik dengan nilai error terkecil sebesar 0,0469. Selanjutnya modifikasi AES dilakukan dengan menggunakan perfect SAC S-box dan matriks M0 Clefia. Hasil uji strict avalanche criterion (SAC) menggunakan variabel bebas kunci pada algoritma modifikasi AES round kedua memiliki nilai yang lebih baik dengan nilai error rata-rata sebesar 0,0002. Hasil uji avalanche weight distribution (AWD) menggunakan variabel bebas kunci dan plaintext pada algoritma modifikasi AES round kedua memiliki nilai yang lebih baik dengan nilai distorsi rata-rata sebesar 0,0371 dan 0,1529. Waktu kecepatan dekripsi pada modifikasi AES dengan 1.000.000 sampel memiliki waktu yang lebih cepat, yaitu 4,1690 seconds. Berdasarkan hasil uji yang dilakukan, algoritma modifikasi AES memiliki ketahanan keamanan dan performa yang lebih baik dibandingkan dengan algoritma AES asli.

Advanced Encryption Standard (AES) is a standard block cipher algorithm used as an implementation of cryptography. The development of attacks on the AES algorithm has encouraged a lot of research related to modifications to the AES algorithm with the aim of increasing the security of the algorithm and to produce alternatives to encryption algorithms that can be used to secure data. In this study, modifications have been made to AES by replacing the S-box using the perfect SAC S-box in the SubBytes process and using the involutary MDS matrix which is the M0 Clefia matrix in the Mixcolumn process. The Perfect SAC S-box has an exact SAC average value of 0.5. Results Based on the test, it was found that the perfect SAC S-box has a better SAC test result with the smallest error value of 0.0469. Furthermore, AES modification is carried out using the perfect SAC S-box and the M0 Clefia matrix. The results of the strict avalanche criteria (SAC) test using the key-independent variables in the second round of modified AES algorithm have an average error value of 0.0002. The results of the avalanche weight distribution (AWD) test using the key-independent variables and plaintext in the second round of modified AES algorithm have an average distortion value of 0.0371 and 0.1529. Decryption speed time on AES modification with 1,000,000 samples has a faster time, which is 4.1690 seconds. results Based on the tests, the modified AES algorithm has better performance and security resistance than the original AES algorithm"
Depok: Fakultas Teknik Universitas Indonesia, 2022
T-pdf
UI - Tesis Membership  Universitas Indonesia Library
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"Pada penelitian ini, dikemukakan sebuah metode baru berbasis analisis multiresolusi untuk mendeteksi distorsi blok pada gambar digital terkompresi. Gambar digital terkompresi cenderung memiliki artefak codingyang mungkin muncul ketika gambar dikodekan dengan tingkat kompresi yang tinggi. Penelitian ini berfokus pada distorsi blok yang dirasakan signifikan dalam gambar digital terkompresi berbasis blok seperti JPEG. Pada penelitian ini, transformasi Wavelet Haar digunakan untuk mendekomposisi sebuah gambar dan menganalisis karakteristik tepian dari gambar tersebut. Berdasarkan dekomposisi ini, peneliti menyusun sebuah algoritma untuk mendeteksi distorsi blok dengan menganalisis koefisien hasil transformasi wavelet. Hasil eksperimen algoritma terhadap database gambar LIVE menunjukkanhasil yang sangat memuaskan dengan tingkat kesalahan yang rendah.

Abstract
In this study, presented a new method based on multiresolution analysis to detect the distortion of the block in a compressed digital image. Compressed digital image tend to have coding artifacts that may arise when an image is encoded with a high compression rate. This study focuses on a block distortion that significantly perceived in the block-based compressed digital images such as JPEG. In this study, Wavelet Haar transformation is used to decompose an image and analyze the characteristics of the edge of the picture. Based on this decomposition, the researchers compiled an algorithm for detecting a block distortion by analyzing the coefficients of the wavelet transformation. The results of experimental algorithms for image database LIVE shows very satisfactory results with low error rates."
[Fakultas Ilmu Komputer Universitas Indonesia, Universitas Bakrie. Fakultas Teknik dan Ilmu Komputer], 2011
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Artikel Jurnal  Universitas Indonesia Library
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Awcock, G.J.
New York : McGraw-Hill, 1996
621.367 AWC a
Buku Teks  Universitas Indonesia Library
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Muthia Hurina
"Salah satu penggunaan teknologi saat ini adalah menyimpan data dalam format digital. Terkadang data yang disimpan bersifat rahasia, sehingga diperlukan metode untuk menjaga kerahasiaannya. Dua metode yang dapat digunakan untuk menjaga kerahasiaan data adalah kriptografi dan steganografi. Penelitian ini bertujuan membuat metode pengamanan teks digital dengan menyembunyikannya dalam gambar digital dengan menggunakan kriptografi dan steganografi. Metode kriptografi dan steganografi yang digunakan dalam penelitian ini berbasis chaos dengan menggunakan fungsi chaos yang disebut MS Map dan teknik embedding yang disebut LSB dengan pola 3-3-2. Dengan menggunakan aplikasi yang disebut uji Institut Nasional Standar dan Teknologi (NIST), ditemukan bahwa urutan berisi nomor yang dihasilkan oleh MS Map lulus 15 tes dalam tes NIST sehingga dapat disimpulkan bahwa urutan itu acak. Selain itu, analisis ini memperoleh sensitivitas kunci hingga 10−15 dan ruang kunci 1.04976 × 101269. Kualitas gambar steganografi (disebut gambar stego) diukur dengan Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), dan koefisien korelasi. Nilai MSE yang dihasilkan mendekati 0 dengan maksimum 0,177153; nilai PSNR yang dihasilkan di atas 40 dB dengan minimum 55,647312 dB; sedangkan koefisien korelasi yang dihasilkan mendekati 1. Ini menunjukkan bahwa gambar stego tidak dapat dibedakan dengan gambar asli dalam tampilan biasa. Sedangkan untuk teks yang diekstraksi, kualitasnya diukur oleh perbedaan karakter dengan teks asli dan MSE. Nilai yang diperoleh untuk perbedaan karakter dan MSE adalah 0 yang menunjukkan bahwa teks yang diekstraksi sama dengan teks asli.

One use of todays technology is storing data in digital format. Sometimes the data storedis confidential, so a method is needed to maintain its confidentiality. Two methods that can be used to maintain data confidentiality are cryptography and steganography. This research aims to make a method of securing digital text by hiding it in a digital image by using cryptography and steganography. The method of cryptography and steganography used in this research is chaos-based by using chaos function called MS Map and embedding technique called LSB with 3-3-2 pattern. By using an application called National Institute of Standards and Technology (NIST) test, it is found that a sequence contains number generated by MS Map passed 15 tests in NIST test so it can be concluded that the sequenceis random. Furthermore, the analysis obtained key sensitivity up to 10-15and key space of 1,04976×101269. The quality of steganography image (called stego image) is measured by Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and correlation coefficient. The MSE values yielded areclose to 0 with a maximum of 0,177153; the PSNR values yielded are above 40dB with a minimum of 55,647312dB; while the correlation coefficients yielded are close to 1.This shows that the stego image cannot be distinguished with the original image in plain view. As forthe extracted text, its qualityis measured by the character difference with theoriginal text and MSE. The values obtained both for character difference and MSE are 0 which indicates that the extracted text is the same as the original text."
Depok: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2019
S-pdf
UI - Skripsi Membership  Universitas Indonesia Library
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Muhammad Nanda Kurniawan
"In this research, Parrot AR.Drone as an Unmanned Aerial Vehicle (UAV) was used to track an object from above. Development of this system utilized some functions from OpenCV library and Robot Operating System (ROS). Techniques that were implemented in the system are image processing al-gorithm (Centroid-Contour Distance (CCD)), feature extraction algorithm (Principal Component Ana-lysis (PCA)) and an artificial neural network algorithm (Generalized Learning Vector Quantization (GLVQ)). The final result of this research is a program for AR.Drone to track a moving object on the floor in fast response time that is under 1 second.
Pada penelitian ini, Parrot AR.Drone digunakan sebagai pesawat tanpa awak untuk menjejaki sebuah objek dari atas. Pengembangan sistem ini memanfaatkan beberapa fungsi dari pustaka OpenCV dan Robot Operating System (ROS). Teknik-teknik yang diimplementasikan pada sistem yang dikem-bangkan adalah algoritma pengolahan citra (Centroid-Contour Distance (CCD)), algoritma ekstraksi fitur (Principal Component Analysis (PCA)), dan algoritma jaringan syaraf tiruan (Generalized Lear-ning Vector Quantization (GLVQ)). Hasil akhir dari penelitian ini adalah sebuah program untuk AR. Drone yang berfungsi untuk menjejaki sebuah objek bergerak di lantai dengan respon waktu yang ce-pat dibawah satu detik."
Fakultas Ilmu Komputer Universitas Indonesia, 2014
PDF
Artikel Jurnal  Universitas Indonesia Library
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Aniati Murni Arymurthy
"This dissertation describes the synergy use of remote sensing data from multi-temporal and multi sensor (optical and radar) for improving our understanding of the land-cover structural phenomena. A tropical country like Indonesia has a high cloud coverage throughout the year with a maximum during the rainy season, and hence the availability of cloud-free optical images is minimal. To solve this problem, radar images have been intensively introduced. The radar images are cloud-free but their use is hampered due to their speckle noise and topographic distortions, and the lack of a suitable radar image classification system.
In many cases, the use of optical or radar image alone is not sufficient. Therefore, the main objectives of this research are: (i) to develop a framework for multi date and multi sensor (optical and radar) image classification; (ii) to solve the cloud cover problem in optical images; and (iii) to obtain a more consistent image classification using multi date and multi sensor images. We have proposed a framework for multi date and multi sensor image classification based on a uniform image classification scheme. The term uniform means that the same procedure can be used to classify optical or radar images, low-level mosaic or fused images, single or multiple feature images.
To be able to conduct a multi temporal and multi sensor analysis, we have unified the optical and radar image classification procedure after finding that both optical and radar images consist of homogeneous and textured regions. A region is considered as homogeneous if the local variance of gray level distribution is relatively low, and a region is considered as textured if the local variance is high. We used a multivariate Gaussian distribution to model the homogeneous part and a multinomial distribution to model the gray level co-occurrences of the textured part, and applied a multiple classifier system to improve the classification accuracy.
The main advantages of the uniform classification scheme are as follow. First, we can tune the homogeneous-textured threshold value parameter in order to obtain an optimal result by allowing the classifier working as a single (conventional) or multiple classifier system. The classifier can have a better or at least the same classification accuracy as the conventional one. Second, we can use either single-band or multi-band input images. This will make it possible to classify a. radar image based on multi-model texture feature images or to classify multi spectral optical images. Third, we can use the same procedure to classify any input images. Compared to the conventional classifiers, the multiple classifier system can improve the classification result from 0% to 20% for radar images and from 0% to 2% for optical images.
The proposed framework incorporates the image mosaicking and data fusion at the low-level stage (before the classification process) as well as at the high-level stage (after the classification process). For cloud cover removal, the image mosaicking at the low-level stage is usually done using multi temporal optical images, whereas mosaicking at the high-level stage is applied to the classified optical and radar images. To be able to obtain a cloud-free image, we have modified the existing Soofi and Smith algorithm which is using multi temporal optical images to an algorithm using multi sensor images. In the high-level data fusion, we have also been able to incorporate a mechanism for cloud cover removal by omitting the information from the optical sensor and using only the information from the radar sensor. According to a case study in our experiment, the cloud cover removal and image classification using the low-level image mosaicking, the high-level image mosaicking, and the high-level data fusion gave 80.2%, 76.2%, and 80.5% classification accuracy, respectively.
The high-level data fusion combines the decisions from several input images to obtain a consensus of classified image. We have applied both the maximum joint posterior probability and the highest rank method for the decision combination functions. We have utilized two existing data fusion methods and have proposed an alternative data fusion method based on the compound conditional risk. According to the experimental results, the decision combination function based on the maximum joint posterior probability favors the optical feature image, while the highest rank method favors the radar feature image. The preference of using the maximum joint posterior probability results in the domination of optical features in the fusion result, and the classification accuracy of the fused image can be better 8.5% in average than the individual radar classified image."
1997
D235
UI - Disertasi Membership  Universitas Indonesia Library
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