Author

Aris Puji Widodo

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Open access Jul 2026

Block-wise Authenticated DNA-based Image Encryption with Tamper Localization using HKDF-Derived Keys

Confidentiality alone cannot establish whether a medical, forensic, cloud-stored, or remotely sensed image has been modified during transmission, while a single global authentication verdict cannot identify the affected region. This paper presents a block-wise authenticated DNA-based image encryption framework that integrates a DNA-chaos confidentiality core with pre-decryption integrity verification and spatial tamper localization. An authenticated ephemeral X25519 key exchange establishes a session secret, which is expanded by HKDF-SHA256 into four transcript-bound, domain-separated subkeys for permutation, DNA operations, diffusion, and authentication. Chaotic seeds are derived from a canonical plaintext hash and block coordinates, preserving strong plaintext differential sensitivity while confining post-encryption modifications to the affected blocks. Ciphertext integrity is enforced using a block-wise encrypt-then-MAC construction with 128-bit truncated HMAC-SHA256 tags that authenticate both the canonical header and each ciphertext block. A reduction-based security argument shows that the authentication layer provides ciphertext integrity and conditionally upgrades an IND-CPA encryption core to IND-CCA security under standard HKDF and HMAC assumptions, while the confidentiality claim remains explicitly conditional on the encryption core. Experiments on 30 tuberculosis chest radiographs across five independent sessions achieved a ciphertext entropy of 7.9972, near-zero adjacent-pixel correlation, 99.61% NPCR, and 33.47% UACI. Across five representative tampering attacks, the proposed framework achieved an observed 100% block-level true-positive rate, 0% false-positive rate, and required only 2.847 ± 0.258 ms for block-wise authentication with 6.25% tag overhead using the default 16×16 block configuration. These results demonstrate that the proposed framework effectively combines statistical confidentiality, modern cryptographic key management, and reliable block-level tamper localization within a unified authenticated image encryption architecture.

Bagus Satrio Waluyo Poetro, Kusworo Adi, Aris Puji Widodo · Journal of Computing Theorie... · 0 citations

Open access Jul 2026

Robust adaptive feature selection for imbalanced software defect prediction

Software defect prediction (SDP) often faces challenges related to heterogeneous software metrics, classifier dependency, and severe class imbalance, which may limit the robustness and generalization of feature selection strategies. This study proposes an adaptive feature selection approach to construct structured and discriminative feature subsets that remain effective across diverse datasets and learning models. The proposed method first measures the relationship between each software metric and the defect label using absolute determination power and then applies an adaptive retention rule to iteratively retain features with stronger discriminative contribution. The evaluation was conducted on multiple public defect datasets using several classical machine learning classifiers. Unlike approaches optimized for specific classifiers, the proposed strategy emphasizes cross-classifier robustness and imbalance-aware evaluation through defect recall and Matthews correlation coefficient. Experimental results show that the proposed method achieves a competitive average MCC of 0.257 and a defect recall of 0.446 compared with baseline approaches, although the statistical tests do not indicate significant superiority. Therefore, the proposed method should be interpreted as a comparable and stable alternative for feature selection under imbalanced SDP conditions. Stability and statistical analyses further indicate that the proposed method maintains comparable performance across dataset-classifier combinations. In addition, feature compactness analysis shows that the performance gains are attributable to efficient, interpretable feature subsets, highlighting the importance of robustness-oriented feature selection in SDP.

Aris Puji Widodo, Prajanto Wahyu Adi, Y. Ashari et al. · Discover Artificial Intellig... · 0 citations