Discrimination Ability of Assessors in Check-All-That-Apply Tests: Method and Product Development

Binary similarity measures have been used in several research fields, but their application in sensory data analysis is limited as of yet. Since check-all-that-apply (CATA) data consist of binary answers from the participants, binary similarity measures seem to be a natural choice for their evaluation. This work aims to define the discrimination ability of CATA participants by calculating the consensus values of 44 binary similarity measures. The proposed methodology consists of three steps: (i) calculating the binary similarity values of the assessors, sample pair-wise; (ii) clustering participants into good and poor discriminators based on their binary similarity values; (iii) performing correspondence analysis on the CATA data of the two clusters. Results of three case studies are presented, highlighting that a simple clustering based on the computed binary similarity measures results in higher quality correspondence analysis with more significant attributes, as well as better sample discrimination (even according to overall liking).

Analisis Perbandingan Ketahanan Metode Steganografi LSB dan BPCS Menggunakan Metode Steganalisis Binary Similarity Measures

Least significant bit (LSB) and Bit Plane Complexity Segmentation (BPCS) are two of the most commonly used steganoraphy methods. LSB is relatively simple and can be quickly implemented while BPCS offers an advantage in the capacity of storing hidden messages. These two methods are considered good if and only if the hidden messages in each of them are robust from a steganalysis implementation. This research specifically performs the robustness checks for both methods by using the Binary Similarity Measures (BSM). BSM measures the correlations between bits in a bit-plane to detect the message hidden in an image. Our test results show that the larger the size of the message hidden by using the BPCS method, the smaller is its detection probability. On the contrary, the size of the hidden message is directly proportional to its probability of being discovered in the LSB method. Keywords: steganography, Least Significant Bit, Bit Plane Complexity Segmentation, steganalysis, Binary Similarity Measures Abstrak. Least Significant Bit (LSB) dan Bit Plane Complexity Segmentation (BPCS) merupakan dua metode steganografi yang umum digunakan. LSB dapat diimplementasikan secara cepat dan sederhana sedangkan BPCS menawarkan kelebihan dalam penampungan kapasitas pesan rahasia. Agar dapat dikatakan sebagai metode steganografi yang baik maka kedua metode tersebut harus dapat mempertahankan pesan yang disisipkan dari serangan metode steganalisis. Penelitian yang dilakukan bertujuan untuk mengetahui ketahanan dari masing-masing metode menggunakan metode steganalisis Binary Similarity Measures (BSM). BSM mengukur korelasi antar bit-bit dalam suatu bit-plane untuk mengetahui keberadaan pesan pada citra. Hasil penelitian mengungkapkan bahwa semakin besar pesan yang disisipkan pada suatu citra menggunakan metode BPCS, maka kemungkinan terdeteksinya pesan akan berkurang. Hal ini berbanding terbalik dengan metode LSB dimana ukuran pesan yang disisipkan berbanding lurus dengan kemungkinan terdeteksinya pesan tersebut.Kata Kunci: Steganografi, Least Significant Bit, Bit Plane Complexity Segmentation, Steganalisis, Binary Similarity Measures