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All Journal Infotech Journal Phenomenon : Jurnal Pendidikan MIPA JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Faktor Exacta DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Community Empowerment ELECTRA : Electrical Engineering Articles Abditeknika - Jurnal Pengabdian Kepada Masyarakat EPSILON: Journal of Electrical Engineering and Information Technology J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Jurnal INFOTEL Jurnal Pendidikan Teknologi Informasi (JUKANTI) Telecommunications, Networks, Electronics, and Computer Technologies (TELNECT) Integrated : Journal Of Information Technology and Vocational Education
Endah Setyowati
Universitas Pendidikan Indonesia
Aerospace Engineering Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Performance Limit of Handcrafted Features in Cassavia LSB Steganalysis Mukhamad Salman Nurdin; Endah Setyowati; Galura Muhammad Suranegara
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 2 (2026): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i2.3983

Abstract

Handcrafted feature based steganalysis remains widely used in resource constrained environments despite rapid progress in deep learning detectors. This study investigates the performance limit of compact handcrafted features for binary classification of cover and stego images in Least Significant Bit (LSB) steganalysis on the Cassavia dataset. Five representative models deep neural network (DNN), one dimensional convolutional neural network (1D CNN), random forest, Light Gradient Boosting Machine (LightGBM), and SMOTE enhanced DNN are trained on 44,000 images using 16 descriptors that combine statistical LSB measures with a reduced subset of Spatial Rich Model (SRM) residual features. All models converge to a narrow accuracy band of 72.58-75.50% with Area Under Curve (AUC) values close to 0.50 and pronounced overfitting in the training–validation curves, indicating that the dominant bottleneck arises from limited feature expressivity rather than model capacity or implementation errors. Feature importance analysis further reveals that only a small subset of descriptors contributes substantially, exposing strong redundancy in the handcrafted feature set. Within this CPU friendly LSB based setting, these results establish a practical performance ceiling that is shared across both classical and deep models, while highlighting LightGBM as an attractive option for embedded steganalysis and motivating future hybrid designs that combine handcrafted statistical priors with learned deep representations.
Co-Authors AA Sudharmawan, AA Afridha Laily Alindra Alfin Syawalan andi muhammad Anggie Wijaya Arvina Putri Rachman Carissa Elfrida Cronica Better Marito Manurung Dhiya Lutfia Mahendro Diar Dwi Sutia Dwi Wahyu Riani Farhan Taqi Ghani Fauzan Maulana Wijaya Fauziyah Rhaudhatul Jannah Fitri Novianti Hidayah Fitri Nuraeni Galuh Inti Aulia Galura Muhammad Suranegara Grace Septiana Magdalena Siagian Hadi Putri, Dewi Indriati Hafiziani Eka Putri, Hafiziani Eka Hasyyati Shabrina Ichwan Nul Ichsan Inda Ahmayani Karin Nisrina Andriyani Khalifah Audya Eka Putri Khoirul Anwar maulidia sita Muhamad Dzikri Danuarteu Muhammad Gani Baihaqi Darussalam Muhammad Hilmi Assidiqi Muhammad Idrus Syaban muhammad wildan Mukhamad Salman Nurdin Nenden Permas Hikmatunisa Nissa Restyasari Pratama, Hafiyyan Putra Raka Yudistira Reyna Bethania Berutu Reza Nurfaudzan Ashsiddiq Salwa Tasya Fathira Purba Silvy Zuhruffiatun Nissa Syifaul Fuada Talitha Naila Citra Yasya Dwinida Zahra Zakiyatus Shalihah