A new approach for enhancing LSB steganography using bidirectional coding scheme

This paper proposes a new algorithm for embedding private information within a cover image. Unlike all other already existing algorithms, this one tends to employ the data of the carrier image more efficiently such that the image looks less distorted. As a consequence, the private data is maintained unperceived and the sent information stays unsuspicious.  This task is achieved by dividing the least significant bit plane of the cover image into fixed size blocks, and then embedding the required top-secret message within each block using one of two opposite ways depending on the extent of similarity of each block with the private information needed to be hidden. This technique will contribute to lessen the number of bits needed to be changed in the cover image to accommodate the private data, and hence will substantially reduce the   amount of distortion in the stego-image when compared to the classic LSB image steganography algorithms

Hiding in Plain Sight: Scrubbing Unwanted Information

Steganography is a technique used to hide encrypted messages within multimedia files. This technique was recently reported to have been used by Osama Bin Laden to communicate with terrorist cells within the United States, and, thus, prevention of the transmission of steganographic content is of great interest to those interested in information security. Methods of steganalysis have been developed that attempt to detect files that contain steganographic content. However, authors of these methods admit that they are not viable for production or have been shown to be defeated by newer advances in steganography. This design science research illustrates an innovation in which algorithms neutralize any hidden messages without significantly detracting from the underlying integrity of the multimedia files and without the need for prior detection of steganographic content

STEGANOGRAFI MENGGUNAKAN BLOK PERMUTASI DAN ALGORITMA GENETIKA

Dalam tugas akhir ini dilakukan proses steganografi dengan kombinasi antara Block Permutation Image Steganography (BPIS) dan algoritma genetika. Steganografi merupakan seni dan ilmu menulis pesan secara tersembunyi atau teknik untuk menyembunyikan sebuah pesan sehingga selain pengirim dan penerima tidak ada nyang mengetahui atau menyadari bahwa terdapat suatu pesan rahasia. Salah satu teknik yang dapat digunakan dalam proses ini adalah Block Permutation Image Steganography (BPIS). BPIS adalah algoritma yang berfungsi merubah pesan atau informasi rahasia ke dalam bentuk urutan sekumpulan biner, kemudian dari urutan biner yang ada diacak dengan menggunakan vektor permutasi. Pada akhirnya hasil dari algoritma BPIS akan diolah kembali dengan menggunakan algoritma genetika serta pendekatan Least Significant Bit (LSB). Hipotesis awal tugas akhir ini adalah Block Permutation Image Steganography (BPIS) dan algoritma genetika dengan teknik spatial domain dapat digunakan dalam proses optimasi penyisipan pesan text pada citra digital dengan format bitmap (.bmp) sehingga akan memiliki tingkat keamanan yang lebih tinggi dan kualitas citra digital tetap baik. Dari hasil penelitian dan uji coba yang telah dilakukan menunjukkan bahwa, kombinasi metode blok permutasi dan algoritma genetika dapat digunakan pada steganografi. Sehingga pesan rahasia dapat disisipkan pada media cover image dan diekstraksi kembali dari stego image. Dengan menerapkan metode blok permutasi maka sistem memiliki tingkat keamanan yang lebih tinggi, serta dengan algoritma genetika maka letak penyisipan pesan dapat dioptimasi dan kualitas citra akan tetap terjaga

Optimization of medical image steganography using n-decomposition genetic algorithm

Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications