Pixel grouping of digital images for reversible data hiding

Pixel Grouping (PG) of digital images has been a key consideration in recent development of the Reversible Data Hiding (RDH) schemes. While a PG kernel with neighborhood pixels helps compute image groups for better embedding rate-distortion performance, only horizontal neighborhood pixel group of size 1×3 has so far been considered. In this paper, we formulate PG kernels of sizes 3×1, 2×3 and 3×2 and investigate their effect on the rate-distortion performance of a prominent PG-based RDH scheme. Specially, a kernel of size 3×2 (or 2×3) that creates a pair of pixel-trios having triangular shape and offers a greater possible correlation among the pixels. This kernel thus can be better utilized for improving a PG-based RDH scheme. Considering this, we develop and present an improved PG-based RDH scheme and the computational models of its key processes. Experimental results demonstrated that our proposed RDH scheme offers reasonably better  embedding rate-distortion performance than the original scheme

AN EFFECTIVE REVERSIBLE DATA HIDING METHOD BASED ON PIXEL-VALUE-ORDERING

This paper presents a new effective reversible data hiding method based on pixel-value-ordering (iGePVO-K) which is improvement of a recent GePVO-K method that recently is considered as a PVO-used method having highest embedding capacity. In comparison with GePVO-K method, iGePVO-K has the following advantages. First, the embedding capacity of the new method is higher than that of GePVO-K method by using data embedding formulas reasonably and reducing the location map size. Second, for embedding data, in the new method, each pixel value is modified at most by one, while in GePVO-K method, each pixel value may be modified by two. In fact, in the GePVO-K method, the largest pixels are modified by two for embedding bits 1 and by one for bits 0. This is also true for the smallest pixels. Meanwhile, in the proposed method, the largest pixels are modified by one for embedding bits 1 and are unchanged if embedding bits 0. Therefore, the stego-image quality in proposed method is better than that in GePVO-K method. Theoretical analysis and experiment results show that the proposed method has higher embedding capacity and better stego image quality than GePVO-K method

Enhancement Of Pixel Value Ordering Based Data Hiding By Row Block Partition

The development of information and communication technology that support digital data transmission such as text, image, audio and video gives several effects. One of them is data security that becomes the main priority during the transmission process. Pixel-Value-Ordering (PVO) which one of data hiding methods can be implemented to achieve the requirement. It embeds data on maximum pixel and minimum pixel in a blok which is a part of the carrier image. However, PVO has capacity a problem, that only 2 bits per block can be hidden. To handle this problem, we propose a new approach by dividing blocks dinamically based on its complexity. These blocks are grouped into 4: smooth block, semi-smooth block, normal block and rough block. Using this approach, the stego capacity can be improved up to 2.6 times in average of previous method by keeping the quality stego more than 65 dB for all testing image

ENHANCEMENT OF PIXEL VALUE ORDERING BASED DATA HIDING BY ROW BLOCK PARTITION

The development of information and communication technology that support digital data transmission such as text, image, audio and video gives several effects. One of them is data security that becomes the main priority during the transmission process. Pixel-Value-Ordering (PVO) which one of data hiding methods can be implemented to achieve the requirement. It embeds data on maximum pixel and minimum pixel in a blok which is a part of the carrier image. However, PVO has capacity a problem, that only 2 bits per block can be hidden. To handle this problem, we propose a new approach by dividing blocks dinamically based on its complexity. These blocks are grouped into 4: smooth block, semi-smooth block, normal block and rough block. Using this approach, the stego capacity can be improved up to 2.6 times in average of  previous method by keeping the quality stego more than 65 dB for all testing image

Peningkatan Kualitas Citra Stego pada Adaptive Pixel Block Grouping Reduction Error Expansion dengan Variasi Model Scanning pada Pembentukan Kelompok Piksel

Kebutuhan komunikasi yang terus bertambah dan ditandai dengan meningkatnya jumlah IP traffic dari 744 EB menjadi 1.164 EB menjadikan keamanan sebagai salah satu kebutuhan utama dalam menjaga kerahasiaan data. Adaptive Pixel Block Grouping Reduction Error Expansion (APBG-REE) sebagai salah satu metode data hiding dapat diterapkan untuk memenuhi kebutuhan tersebut. Metode ini akan membagi citra carrier menjadi blok-blok dan membentuknya menjadi kelompok-kelompok piksel. Hasil dari proses ini akan dimanfaatkan untuk menyembunyikan data rahasia. Namun, metode ini memiliki kekurangan, yaitu belum diketahuinya metode scanning terbaik dalam pembentukan kelompok piksel untuk menciptakan citra stego dengan kualitas tinggi. Untuk mengatasi masalah ini, kami mengusulkan 4 mode (cara) scanning berdasarkan arah scanning tersebut. Mode scanning tersebut memberikan hasil yang berbeda-beda untuk masing-masing citra stego yang diujikan. Namun berdasarkan hasil uji coba, setiap mode scanning mampu menjaga kualitas citra stego diatas 57,5 dB. Hasil ini akan meningkat seiring dengan berkurangnya jumlah shifted pixel yang terbentuk.   Abstract The need of communication has increased continously which is represented by the rise of number of IP traffic, from 744 EB to 1.164 EB. This has made data security one of the main requirements in terms of securing secret data. Adaptive Pixel Block Grouping Reduction Error Expansion (APBG-REE) as one of data hiding methods can be implemented to meet that requirement. It divides the carrier image into blocks which are then used as pixel groups. The result of this process is to be a space for secret data. However, this method has a problem in the scanning when creating pixel groups to generate a high quality stego image. To handle this problem, we propose four scanning models base on its direction. This means that the scanning can be done row-by-row or column-by-column. Base on the experiment, we find that those modes deliver various results and each of them is able to maintain the stego quality of more than 57,5 dB. This result increases along with the decreasing the number of shifted pixels

Adaptive Reversible Data Hiding Scheme for Digital Images Based on Histogram Shifting

Existing histogram based reversible data hiding schemes use only absolute difference values between the neighboring pixels of a cover image. In these schemes, maxima and minima points at maximum distance are selected in all the blocks of the image which causes shifting of the large number of pixels to embed the secret data. This shifting produces more degradation in the visual quality of the marked image. In this work, the cover image is segmented into blocks, which are classified further into complex and smooth blocks using a threshold value. This threshold value is optimized using firefly algorithm. Simple difference values between the neighboring pixels of complex blocks have been utilized to embed the secret data bits. The closest maxima and minima points in the histogram of the difference blocks are selected so that number of shifted pixels get reduced, which further reduces the distortion in the marked image. Experimental results prove that the proposed scheme has better performance as compared to the existing schemes. The scheme shows minimum distortion and large embedding capacity. Novelty of work is the usage of negative difference values of complex blocks for secret data embedding with the minimal number of pixel shifting

Cyber Security and Critical Infrastructures

This book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles: an editorial explaining current challenges, innovative solutions, real-world experiences including critical infrastructure, 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems, and a review of cloud, edge computing, and fog's security and privacy issues