Multilevel Secure Digital Image Steganography Framework Using Random Function and Advanced Encryption Standard

2019 ◽  
Vol 16 (11) ◽  
pp. 4812-4825
Author(s):  
Mohsin N. Srayyih Almaliki
Keyword(s):  
Image Quality ◽  
Random Function ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Secret Message ◽  
Least Significant Bit ◽  
Signal To Noise ◽  
Stego Image ◽  
Preparation Phase ◽  
Noise Ratio

One of the crucial aspects of processes and methodologies in the information and communication technology era is the security of information. The security of information should be a key priority in the secret exchange of information between two parties. In order to ensure the security of information, there are some strategies which are used, and they include steganography and cryptography. With cryptography, the secret message is converted into unintelligible text, but the existence of the secret message is noticed, nonetheless, steganography involves hiding the secret message in a way that its presence cannot be noticed. In this paper, a new secure image steganography framework which is known as an adaptive stego key LSB (ASK-LSB) framework is proposed. The construction of the proposed framework was carried out in four phases with the aim of improving the data-hiding algorithm in cover images by using capacity, image quality, and security. To achieve this, the Peak Signal-to-Noise Ratio (PSNR) of the steganography framework was maintained. The four phases began with the image preparation phase, followed by the secret message preparation phase, embedding phase and finally extraction phase. The secure image steganography framework that is proposed in this study is based on a new adaptive of least significant bit substitution method, combination random function, and encryption method. In the proposed work, the secret bits are inserted directly or inversely, thereby enhancing the imperceptibility and complexity of the process of embedding. Results from the experiment reveal that the algorithm has better image quality index, peak signal-to-noise ratio, and payload used in the evaluation of the stego image.

scholarly journals Least Significant Braille Method in Steganography Using Digital Image Media For Security Message

2017 ◽  
Vol 6 (1) ◽  
pp. 15-20
Author(s):  
Megah Mulya ◽  
Zikry Sugiwa
Keyword(s):  
Digital Image ◽  
Signal To Noise Ratio ◽  
Human Vision ◽  
Secret Message ◽  
Signal To Noise ◽  
Stego Image ◽  
Resistance Level ◽  
Noise Ratio

Confidentiality of the message or the information is the most important and essential.  It is very influential on the party who has the valuable message when they want to exchange messages on others.  To keep the message is not known to others, the necessary security on the message.  Steganography is one technique for providing security to the message.  Steganography is a technique to hide messages in a medium, such as pictures, sounds and video.  Steganographic technique used in this study is the Least Significant Braille (LSBraille).  This technique makes use of human vision in the message on the bit value was not significant.  This study focuses on how much resistance level stego image to various image processes and measure results accuracy Peak Signal to Noise Ratio (PSNR).  From the result of the insertion of a secret message, that the level of resistance stego image is not resistant to digital image processing.  The result of the calculation of PSNR value obtained from experiments on all data samples between 51-73 db.

scholarly journals Increase the PSNR Of Image Using LZW and AES Algorithm With MLSB on Steganography

2018 ◽  
Vol 7 (2.5) ◽  
pp. 119
Author(s):  
Erick Fitra Wijayanto ◽  
Muhammad Zarlis ◽  
Zakarias Situmorang
Keyword(s):  
Signal To Noise Ratio ◽  
Text Messages ◽  
Test Reliability ◽  
Text File ◽  
Least Significant Bit ◽  
Signal To Noise ◽  
Stego Image ◽  
Aes Algorithm ◽  
Noise Ratio ◽  
Lzw Algorithm

There are many research has done a hybridization approach of text message insertion that has been compressed with Lempel-Ziv-Welch (LZW) algorithm and has also been encrypted. The text messages in ciphertext form are inserted into the image file using LSB (Least Significant Bit) method. The results of this study indicate that the value of Peak Signal to Noise Ratio (PSNR) lower than the LSB method of 0.94 times with a ratio of 20.33%, with Kekre's method of 10.04%. To improve the value of PSNR stego image of insertion, in this research is inserted audio samples using 5 bits to reduce the amount of data insertion, so it can get the value of MSE stego image low. Prior to insertion, the text file is compressed with the Lempel-Ziv-Welch (LZW) algorithm and encrypted with the Advanced Encryption Standard (AES) algorithm. Then, the insertion of compression and encrypted text files is done with the Modified Least Significant Bit (MLSB) algorithm. To performa test reliability of steganography, the image stego image is done by calculating Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). At extraction process with MLSB algorithm, decryption with AES algorithm and decompression with LZW algorithm. The experimental results show that the MSE values obtained are lower and the proposed PSNR method is better with (α) 1,044 times than the Kaur method, et al. The result of embed text file extraction from the stego image works well resulting in encrypted and uncompressed text files.  

scholarly journals Steganografi Citra Menggunakan Metode Least Significant Bit (LSB) dan Linear Congruential Generator (LCG)

2019 ◽  
Vol 5 (2) ◽  
pp. 137-146
Author(s):  
Andre Hernandes ◽  
Hartini Hartini ◽  
Dewi Sartika
Keyword(s):  
Signal To Noise Ratio ◽  
Least Significant Bit ◽  
Signal To Noise ◽  
Stego Image ◽  
Congruential Generator ◽  
Linear Congruential Generator ◽  
Noise Ratio ◽  
Linear Congruential

Steganografi merupakan sebuah cara yang digunakan untuk menyembunyi-kan pesan rahasia dari orang yang tidak berhak mengetahuinya. Pada penelitian ini penulis menggunakan salah satu metode steganografi yaitu metode least significant bit (LSB) untuk menyisipkan bit-bit pesan rahasia berupa teks kedalam citra digital RGB berekstensi file bitmap, dengan cara menggabungkan metode LSB dan linear congruential generator (LCG) untuk membangkitkan bilangan acak dari posisi pixel yang akan disisipkan pesan rahasia. Hasil dari penelitian ini penulis berhasil membangun aplikasi steganografi dengan bahasa pemrograman java dan menguji kualitas stego image yang menghasilkan nilai rata-rata Peak Signal to Noise Ratio (PSNR) yang mencapai 51 dB. Dari penilaian ini, disimpulkan bahwa stego image yang dihasilkan dalam kualitas baik dan tidak mengalami perubahan yang signifikan.

scholarly journals TEKNIK PENGUJIAN KEAMANAN DATA TEXT BERTINGKAT DENGAN METODE STEGANOGRAPHY LSB DAN TEKNIK ENKRIPSI

2018 ◽  
Vol 8 (2) ◽  
pp. 109-122
Author(s):  
Hillman Akhyar Damanik ◽  
Merry Anggraeni
Keyword(s):  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Cover Image ◽  
Mean Square ◽  
Least Significant Bit ◽  
Signal To Noise ◽  
Stego Image ◽  
Noise Ratio

Internet adalah media komunikasi paling populer saat ini, tetapi komunikasi melalui internet menghadapi beberapa masalah seperti keamanan data, kontrol hak cipta, kapasitas ukuran data, otentikasi dan lain sebagainya. Pada penelitian ini peneliti memperkenalkan skema untuk menyembunyikan data yang terenkripsi. Dengan menggunakan citra sebagai embedding dan cover image untuk text hiding. Fitur utama skema adalah cara penyematan data teks ke cover image terenkripsi. Peneliti berkonsentrasi menggunakan metode Red-Green-Blue Least Significant Bit (RGB-LSB). Penyematan data teks dan memverifikasi kinerja menggunakan metode RGB-LSB dalam hal indeks kualitas yaitu Peak Signal-to-Noise Ratio (PSNR) dan Mean Square Error (MSE) , imperceptibility dan indeks recovery. Nilai SME pada jumlah pesan yang disisipi sebanyak 407 kata adalah nilai MSE 0.8310 dan nilai PSNR 48.9348. pada jumlah pesan yang disisipi sebanyak 507 kata adalah nilai MSE 0.8322 dan nilai PSNR 48.9285. Nilai kriteria imperceptibility pada stego image menghasilkan image dan nilai-nilai pixel pada masing-masing cover image tidak mengalami perubahan. Aspek recovery pada penyembunyian pesan teks pada masing-masing cover image pada proses embedding, dimensi citra yang berbeda dan sampai proses ekstraksi juga menghasilkan panjang pesan asli terungkap. Berdasarkan hasil perbandingan ini dapat diketahui bahwa algoritma LSB memiliki hasil yang baik pada teknik penyisipan sebuah pesan pada file citra.

scholarly journals Quality Factor terhadap Kapasitas Pesan Rahasia pada Steganografi Citra JPEG dan Kualitas Citra Stego

2020 ◽  
Vol 6 (2) ◽  
pp. 89-100
Author(s):  
Dian Hafidh Zulfikar
Keyword(s):  
Image Quality ◽  
Quality Factor ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Secret Message ◽  
Q Factor ◽  
Stego Image ◽  
Jpeg Image ◽  
Human Eye

One of the container media that is available and popular is the Joint Photographic Experts Group (JPEG) format image. This article aims to determine the effect of Quality Factor on the secret message capacity of JPEG image steganography and stego image quality. The quality of an image can actually be seen subjectively with the human eye, but this is relative between each individual. Because the assessment of the human eye varies from person to person. In addition, the effect of Quality Factor on secret message capacity is not yet known whether it has an impact. Therefore, in this study the Quality Factor is used to objectively see the secret message capacity of the JPEG image steganography and the quality of the stego image. The parameter used to determine the quality of an image is the Peak Signal to Noise Ratio (PSNR). PSNR will compare the quality of the original image (before steganography) with the stego image. The test results show that the Q Factor effect can affect the secret message capacity of the JPEG image steganography and the stego image quality. The bigger the Q Factor, the more the message capacity is generated. The greater the Q factor, the better the quality of the resulting stego image.

scholarly journals TEKNIK PENGUJIAN KEAMANAN DATA TEXT BERTINGKAT DENGAN METODE STEGANOGRAPHY LSB DAN TEKNIK ENKRIPSI

2018 ◽  
Vol 8 (2) ◽  
pp. 109
Author(s):  
Hillman Akhyar Damanik ◽  
Merry Anggraeni
Keyword(s):  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Cover Image ◽  
Mean Square ◽  
Least Significant Bit ◽  
Signal To Noise ◽  
Stego Image ◽  
Noise Ratio

<p><strong>Internet adalah media komunikasi paling populer saat ini, tetapi komunikasi melalui internet menghadapi beberapa masalah seperti keamanan data, kontrol hak cipta, kapasitas ukuran data, otentikasi dan lain sebagainya. Pada penelitian ini peneliti memperkenalkan skema untuk menyembunyikan data yang terenkripsi. Dengan menggunakan citra sebagai <em>embedding</em> dan <em>cover image</em> untuk <em>text hiding</em>. Fitur utama skema adalah cara penyematan data teks ke <em>cover image</em> terenkripsi. Peneliti berkonsentrasi menggunakan metode Red-Green-Blue Least Significant Bit (RGB-LSB</strong>).<strong> Penyematan data teks dan memverifikasi kinerja menggunakan metode RGB-LSB dalam hal indeks kualitas yaitu </strong><strong>Peak Signal-to-Noise Ratio (PSNR) dan</strong><em> </em><strong>Mean Square Error<em> </em>(MSE)</strong><strong> </strong><strong>, <em>imperceptibility</em> dan indeks<em> recovery</em>. Nilai SME pada jumlah pesan yang disisipi sebanyak 407 kata adalah nilai MSE 0.8310 dan nilai PSNR 48.9348. pada jumlah pesan yang disisipi sebanyak 507 kata adalah nilai MSE 0.8322 dan nilai PSNR 48.9285. Nilai kriteria <em>imperceptibility</em> pada <em>stego image</em> menghasilkan <em>image</em> dan nilai-nilai <em>pixel </em>pada masing-masing <em>cover image</em> tidak mengalami perubahan. Aspek <em>recovery</em> pada penyembunyian pesan teks pada masing-masing <em>cover image</em> pada proses <em>embedding</em>, dimensi citra yang berbeda dan sampai proses ekstraksi juga menghasilkan panjang pesan asli terungkap. Berdasarkan hasil perbandingan ini dapat diketahui bahwa algoritma LSB memiliki hasil yang baik pada teknik penyisipan sebuah pesan pada file citra.</strong></p>

Efficient Steganography Algorithm Based On DCT And Entropy Thresholding Technique

2018 ◽  
Vol 8 (1) ◽  
pp. 45
Author(s):  
Satvir Singh
Keyword(s):  
Relative Entropy ◽  
Experimental Work ◽  
Random Function ◽  
Signal To Noise Ratio ◽  
Binary Sequence ◽  
Threshold Value ◽  
Absolute Difference ◽  
Secret Message ◽  
Signal To Noise ◽  
Confidential Information

Steganography is the special art of hidding important and confidential information in appropriate multimedia carrier. It also restrict the detection of  hidden messages. In this paper we proposes steganographic method based on dct and entropy thresholding technique. The steganographic algorithm uses random function in order to select block of the image where the elements of the binary sequence of a secret message will be inserted. Insertion takes place at the lower frequency  AC coefficients of the  block. Before we insert the secret  message. Image under goes dc transformations after insertion of the secret message we apply inverse dc transformations. Secret message will only be inserted into a particular block if  entropy value of that particular block is greater then threshold value of the entropy and if block is selected by the random function. In  Experimental work we calculated the peak signal to noise ratio(PSNR), Absolute difference , Relative entropy. Proposed algorithm give high value of PSNR  and low value of Absolute difference which clearly indicate level of distortion in image due to insertion of secret message is reduced. Also value of  relative entropy is close to zero which clearly indicate proposed algorithm is sufficiently secure. 

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations

2021 ◽  
pp. 197140092110087
Author(s):  
Andrea De Vito ◽  
Cesare Maino ◽  
Sophie Lombardi ◽  
Maria Ragusi ◽  
Cammillo Talei Franzesi ◽  
...  
Keyword(s):  
Image Quality ◽  
Iterative Reconstruction ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Signal To Noise ◽  
Subjective Image Quality ◽  
Model Based ◽  
Hybrid Iterative Reconstruction ◽  
Noise Ratio ◽  
Enhanced Computed Tomography

Background and purpose To evaluate the added value of a model-based reconstruction algorithm in the assessment of acute traumatic brain lesions in emergency non-enhanced computed tomography, in comparison with a standard hybrid iterative reconstruction approach. Materials and methods We retrospectively evaluated a total of 350 patients who underwent a 256-row non-enhanced computed tomography scan at the emergency department for brain trauma. Images were reconstructed both with hybrid and model-based iterative algorithm. Two radiologists, blinded to clinical data, recorded the presence, nature, number, and location of acute findings. Subjective image quality was performed using a 4-point scale. Objective image quality was determined by computing the signal-to-noise ratio and contrast-to-noise ratio. The agreement between the two readers was evaluated using k-statistics. Results A subjective image quality analysis using model-based iterative reconstruction gave a higher detection rate of acute trauma-related lesions in comparison to hybrid iterative reconstruction (extradural haematomas 116 vs. 68, subdural haemorrhages 162 vs. 98, subarachnoid haemorrhages 118 vs. 78, parenchymal haemorrhages 94 vs. 64, contusive lesions 36 vs. 28, diffuse axonal injuries 75 vs. 31; all P<0.001). Inter-observer agreement was moderate to excellent in evaluating all injuries (extradural haematomas k=0.79, subdural haemorrhages k=0.82, subarachnoid haemorrhages k=0.91, parenchymal haemorrhages k=0.98, contusive lesions k=0.88, diffuse axonal injuries k=0.70). Quantitatively, the mean standard deviation of the thalamus on model-based iterative reconstruction images was lower in comparison to hybrid iterative one (2.12 ± 0.92 vsa 3.52 ± 1.10; P=0.030) while the contrast-to-noise ratio and signal-to-noise ratio were significantly higher (contrast-to-noise ratio 3.06 ± 0.55 vs. 1.55 ± 0.68, signal-to-noise ratio 14.51 ± 1.78 vs. 8.62 ± 1.88; P<0.0001). Median subjective image quality values for model-based iterative reconstruction were significantly higher ( P=0.003). Conclusion Model-based iterative reconstruction, offering a higher image quality at a thinner slice, allowed the identification of a higher number of acute traumatic lesions than hybrid iterative reconstruction, with a significant reduction of noise.

Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

2020 ◽  
Vol 4 (2) ◽  
pp. 53-60
Author(s):  
Latifah Listyalina ◽  
Yudianingsih Yudianingsih ◽  
Dhimas Arief Dharmawan
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Image Quality ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Vital Role ◽  
Retinal Images ◽  
Signal To Noise ◽  
Noise Ratio

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

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