Performance Evaluation of Steganogan Using Hybrid Image Datasets and Statistical Quality Metrics

Metin Eken; İclal Çetin Taş

doi:10.53070/bbd.2027695

EN TR

Performance Evaluation of Steganogan Using Hybrid Image Datasets and Statistical Quality Metrics

Abstract

This study proposes a novel approach by employing SteganoGAN, a steganographic framework based on Generative Adversarial Networks (GANs). The impact of image categories with different semantic contents (art, building, and nature) on system performance was examined in detail using the SteganoGAN architecture. A hybrid dataset composed of BossBase and USC-SIPI images was utilized to conduct the experiments. The influence of image quality, categorized by visual content, on performance was systematically analyzed. In particular, changes in skewness and kurtosis values, as well as the widely used image quality metrics PSNR and SSIM, were investigated. Experimental results demonstrated that nature images provided superior performance, achieving an SSIM value of 99.59 and exhibiting the lowest skewness deviation from the original cover images.

Keywords

References

Abdulwahed, M. N. (2020). An effective and secure digital image steganography scheme using two random function and chaotic map.
Abuzanouneh, K. I. M., & Hadwan, M. (2021). Multi-stage protection using pixel selection technique for enhancing steganography. International Journal of Communication Networks and Information Security (IJCNIS), 13(1), 55–61. https://doi.org/10.17762/IJCNIS.V13I1.4907
Ahmad, S., Ogala, J. O., Ikpotokin, F., Arif, M., Ahmad, J., & Mehfuz, S. (2024). Enhanced CNN-DCT steganography: Deep learning-based image steganography over cloud. SN Computer Science, 5(4). https://doi.org/10.1007/S42979-024-02756-X
Alanzy, M., Alomrani, R., Alqarni, B., & Almutairi, S. (2023). Image steganography using LSB and hybrid encryption algorithms. Applied Sciences, 13(21), 11771. https://doi.org/10.3390/APP132111771
Ali, A., Esam, E., Bayoumi, R., Tawfik, A., Abdulrahman, O., Saad, S., Ashraf, B. M., & Ihab, S. (2023). Steganography platform. Proceedings of the 11th IEEE International Conference on Intelligent Computing and Information Systems (ICICIS 2023), 536–541. https://doi.org/10.1109/ICICIS58388.2023.10391186
An, Y., Yu, N., Zhang, S., Liu, H., & Duan, J. (2022). ACGAN based coverless image steganography method. Proceedings of SPIE, 12294, 1035–1040. https://doi.org/10.1117/12.2639718
Ansari, A. S., Mohammadi, M. S., & Parvez, M. T. (2020). A multiple-format steganography algorithm for color images. IEEE Access, 8, 83926–83939. https://doi.org/10.1109/ACCESS.2020.2991130
Bai, Y., Zhang, Y., Ding, M., & Ghanem, B. (2018). SOD-MTGAN: Small object detection via multi-task generative adversarial network. In European Conference on Computer Vision (Vol. 11217, pp. 210–226). Springer. https://doi.org/10.1007/978-3-030-01261-8_13

Bui, T., Agarwal, S., Yu, N., & Collomosse, J. (2023). RoSteALS: Robust steganography using autoencoder latent space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 933–942). https://doi.org/10.1109 /CVPRW59228.2023.00100
Cai, L., & Wang, W. Y. (2018). KBGAN: Adversarial learning for knowledge graph embeddings. In NAACL HLT 2018: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Vol. 1, pp. 1470–1480). https://doi.org/10.18653/v1/n18-1133
Chatterjee, A., Ghosal, S. K., & Sarkar, R. (2020). LSB based steganography with OCR: An intelligent amalgamation. Multimedia Tools and Applications, 79(17–18), 11747–11765. https://doi.org/10.1007/S11042-019-08472-6
Chen, M., He, P., & Liu, J. (2022). HLTD-CSA: Cover selection algorithm based on hybrid local texture descriptor for color image steganography. Journal of Visual Communication and Image Representation, 89, 103646. https://doi.org/10.1016/J.JVCIR.2022.103646
Dai, W., Dong, N., Wang, Z., Liang, X., Zhang, H., & Xing, E. P. (2018). SCAN: Structure correcting adversarial network for organ segmentation in chest X-rays. In Medical Image Computing and Computer Assisted Intervention (Vol. 11045, pp. 263–273). Springer. https://doi.org/10.1007/978-3-030-00889-5_30
Datta, B., Dutta, K., & Roy, S. (2020). Data hiding in virtual bit-plane using efficient Lucas number sequences. Multimedia Tools and Applications, 79(31–32), 22673–22703. https://doi.org/10.1007/S11042-020-08979-3 DeOldify. (2024). Home. https://deoldify.ai/
Donahue, C., Li, B., & Prabhavalkar, R. (2018). Exploring speech enhancement with generative adversarial networks for robust speech recognition. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5024–5028). https://doi.org/10.1109/ICASSP.2018.8462581
Duan, X., Guo, D., Liu, N., Li, B., Gou, M., & Qin, C. (2020). A new high capacity image steganography method combined with image elliptic curve cryptography and deep neural network. IEEE Access, 8, 25777–25788. https://doi.org/10.1109/ACCESS.2020.2971528
Duan, X., Liu, N., Gou, M., Wang, W., & Qin, C. (2020). SteganoCNN: Image steganography with generalization ability based on convolutional neural network. Entropy, 22(10), 1–15. https://doi.org/10.3390/E22101140
El-Nouby, A., Sharma, S., Schulz, H., Hjelm, D., El Asri, L., Ebrahimi Kahou, S., Bengio, Y., & Taylor, G. W. (2019). Tell, draw, and repeat: Generating and modifying images based on continual linguistic instruction. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 10304–10312.
Fakhredanesh, M., Rahmati, M., & Safabakhsh, R. (2019). Steganography in discrete wavelet transform based on human visual system and cover model. Multimedia Tools and Applications, 78(13), 18475–18502. https://doi.org/10.1007/S11042-019-7238-8
Google for Developers. (n.d.). Overview of GAN structure. https://developers.google.com/machine-learning/gan/gan_structure
Guimaraes, G., Sanchez-Lengeling, B., Outeiral, C., Luis, P., Farias, C., & Aspuru-Guzik, A. (2017). Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models. arXiv. https://arxiv.org/abs/1705.10843v3
Han, Y., Zhang, P., Huang, W., Zha, Y., Cooper, G. D., & Zhang, Y. (2020). Robust visual tracking based on adversarial unlabeled instance generation with label smoothing loss regularization. Pattern Recognition, 97, 107027. https://doi.org/10.1016/J.PATCOG.2019.107027
Hassaballah, M., Hameed, M. A., Awad, A. I., & Muhammad, K. (2021). A novel image steganography method for industrial Internet of Things security. IEEE Transactions on Industrial Informatics, 17(11), 7743–7751. https://doi.org/10.1109/TII.2021.3053595
Hu, W., & Tan, Y. (2017). Generating adversarial malware examples for black-box attacks based on GAN. Communications in Computer and Information Science, 1745, 409–423. https://doi.org/10.1007/978-981-19-8991-9_29
Huo, L., Chen, R., Wei, J., & Huang, L. (2024). A high-capacity and high-security image steganography network based on chaotic mapping and generative adversarial networks. Applied Sciences, 14(3), 1225. https://doi.org/10.3390/APP14031225
Hwang, J.-J., Azernikov, S., Efros, A. A., & Yu, S. X. (2018). Learning beyond human expertise with generative models for dental restorations. arXiv. https://arxiv.org/abs/1804.00064v1
Iizuka, S., Simo-Serra, E., & Ishikawa, H. (2017). Globally and locally consistent image completion. ACM Transactions on Graphics, 36(4). https://doi.org/10.1145/3072959.3073659
Kadhim, I. J., Premaratne, P., & Vial, P. J. (2020). High capacity adaptive image steganography with cover region selection using dual-tree complex wavelet transform. Cognitive Systems Research, 60, 20–32. https://doi.org/10.1016/J.COGSYS.2019.11.002
Ke, Y., Zhang, M. Q., Liu, J., Su, T. T., & Yang, X. Y. (2019). Generative steganography with Kerckhoffs’ principle. Multimedia Tools and Applications, 78(10), 13805–13818. https://doi.org/10.1007/S11042-018-6640-
Kordov, K., & Zhelezov, S. (2021). Steganography in color images with random order of pixel selection and encrypted text message embedding. PeerJ Computer Science, 7, 1–21. https://doi.org/10.7717/PEERJ-CS.380
Kuppusamy, P. G., Ramya, K. C., Rani, S. S., Sivaram, M., & Dhasarathan, V. (2020). A novel approach based on modified cycle generative adversarial networks for image steganography. Scalable Computing: Practice and Experience, 21(1), 63–72. https://doi.org/10.12694/SCPE.V21I1.1613
Laishram, D., & Tuithung, T. (2021). A novel minimal distortion-based edge adaptive image steganography scheme using local complexity: (BEASS). Multimedia Tools and Applications, 80(1), 831–854. https://doi.org/10.1007/S11042-020-09519-9
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., & Shi, W. (2017). Photo-realistic single image super-resolution using a generative adversarial network. *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)*, 4681–4690. https://doi.org/10.1109/CVPR.2017.19
Liao, X., Li, K., & Yin, J. (2017). Separable data hiding in encrypted image based on compressive sensing and discrete Fourier transform. Multimedia Tools and Applications, 76(20), 20739–20753. https://doi.org/10.1007/S11042-016-3971-4
Liu, F., Jiao, L., & Tang, X. (2019). Task-oriented GAN for PolSAR image classification and clustering. IEEE Transactions on Neural Networks and Learning Systems, 30(9), 2707–2719. https://doi.org/10.1109/TNNLS.2018.2885799
Liu, Q., Zhou, H., Xu, Q., Liu, X., & Wang, Y. (2021). PSGAN: A generative adversarial network for remote sensing image pan-sharpening. IEEE Transactions on Geoscience and Remote Sensing, 59(12), 10227–10242. https://doi.org/10.1109/TGRS.2020.3042974
Liu, W., Hou, X., Duan, J., & Qiu, G. (2020). End-to-end single image fog removal using enhanced cycle consistent adversarial networks. IEEE Transactions on Image Processing, 29, 7819–7833. https://doi.org/10.1109/TIP.2020.3007844
Lu, S. P., Wang, R., Zhong, T., & Rosin, P. L. (2021). Large-capacity image steganography based on invertible neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10811–10820). https://doi.org/10.1109/CVPR46437.2021.01067
Lutz, S., Amplianitis, K., & Smolic, A. (2018). AlphaGAN: Generative adversarial networks for natural image matting. In British Machine Vision Conference 2018. https://arxiv.org/abs/1807.10088v1
Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., & Van Gool, L. (2017). Pose guided person image generation. In Advances in Neural Information Processing Systems (Vol. 2017-December, pp. 406–416). https://arxiv.org/abs/1705.09368v6
Maji, G., Mandal, S., & Sen, S. (2021). Cover independent image steganography in spatial domain using higher order pixel bits. Multimedia Tools and Applications, 80(10), 15977–16006. https://doi.org/10.1007/S11042-020-10298-6
Mandal, P. C., & Mukherjee, I. (2022). High capacity data hiding based on multi-directional pixel value differencing and decreased difference expansion. Multimedia Tools and Applications, 81(4), 5325–5347. https://doi.org/10.1007/S11042-021-11605-5
Martin, A., Hernández, A., Alazab, M., Jung, J., & Camacho, D. (2023). Evolving generative adversarial networks to improve image steganography. Expert Systems with Applications, 222, 119841. https://doi.org/10.1016/J.ESWA.2023.119841
Miri, A., & Faez, K. (2017). Adaptive image steganography based on transform domain via genetic algorithm. Optik, 145, 158–168. https://doi.org/10.1016/J.IJLEO.2017.07.043
Mogren, O. (2016). C-RNN-GAN: Continuous recurrent neural networks with adversarial training. arXiv. https://arxiv.org/abs/1611.09904v1
Pan, J., Ferrer C.C., McGuinness K., O'Connor N.E., Torres J., Sayrol E. & Giro-i-Nieto X: (2017). SalGAN: Visual saliency prediction with generative adversarial networks. arXiv. https://arxiv.org/abs/1701.01081v3
Pascual, S., Bonafonte, A., & Serra, J. (2017). SEGAN: Speech enhancement generative adversarial network. In Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH) (pp. 3642–3646). https://doi.org/10.21437/Interspeech.2017-1428
Ramandi, V. Y., Fateh, M., & Rezvani, M. (2024). VidaGAN: Adaptive GAN for image steganography. IET Image Processing, 18(12), 3329–3342. https://doi.org/10.1049/IPR2.13177
Ratzlaff, N., & Fuxin, L. (2019). HyperGAN: A generative model for diverse, performant neural networks. In Proceedings of the 36th International Conference on Machine Learning (Vol. 2019-June, pp. 9371–9381). https://arxiv.org/abs/1901.11058v3
Saad, A. H. S., Mohamed, M. S., & Hafez, E. H. (2021). Coverless image steganography based on optical mark recognition and machine learning. IEEE Access, 9, 16522–16531. https://doi.org/10.1109/ACCESS.2021.3050737
Saeed, A., Fawad, Khan, M. J., Shahid, H., Naqvi, S. I., Riaz, M. A., Khan, M. S., & Amin, Y. (2020). An accurate texture complexity estimation for quality-enhanced and secure image steganography. IEEE Access, 8, 21613–21630. https://doi.org/10.1109/ACCESS.2020.2968217
Saeedi, Z., Yazdi, A., Mashhadi, S., Hadian, M., & Gutub, A. (2024). High performance image steganography integrating IWT and Hamming code within secret sharing. IET Image Processing, 18(1), 129–139. https://doi.org/10.1049/IPR2.12938
Saha, S., Chakraborty, A., Chatterjee, A., Dhargupta, S., Ghosal, S. K., & Sarkar, R. (2020). Extended exploiting modification direction based steganography using hashed-weightage array. Multimedia Tools and Applications, 79(29–30), 20973–20993. https://doi.org/10.1007/S11042-020-08951-1
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017). Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In Information Processing in Medical Imaging (Vol. 10265, pp. 146–147). Springer. https://doi.org/10.1007/978-3-319-59050-9_12
Shah, P. D., & Bichkar, R. S. (2020). Genetic algorithm based approach to select suitable cover image for image steganography. In 2020 International Conference for Emerging Technology (INCET 2020). https://doi.org/10.1109/INCET49848.2020.9154032
Shaik, A., & Thanikaiselvan, V. (2019). High capacity reversible data hiding using 2D parabolic interpolation. Multimedia Tools and Applications, 78(8), 9717–9735. https://doi.org/10.1007/S11042-018-6544-X
Shi, H., Dong, J., Wang, W., Qian, Y., & Zhang, X. (2018). SSGAN: Secure steganography based on generative adversarial networks. In Information Security and Cryptology (Vol. 10735, pp. 534–544). Springer. https://doi.org/10.1007/978-3-319-77380-3_51
Shi, W., Wang, Z., Zhang, X., & Feng, G. (2026). StarINN: An efficient invertible neural network for image steganography. Displays, 91, 103175. https://doi.org/10.1016/J.DISPLA.2025.103175
Shmueli, R., Mishra, D., Shmueli, T., & Hadar, O. (2024). A novel technique for image steganography based on maximum energy seam. Multimedia Tools and Applications, 83(28), 70907–70920. https://doi.org/10.1007/S11042-024-18476-6
Shyla, M. K., Kumar, K. B. S., & Das, R. K. (2021). Image steganography using genetic algorithm for cover image selection and embedding. Soft Computing Letters, 3, 100021. https://doi.org/10.1016/J.SOCL.2021.100021
Souly, N., Spampinato, C., & Shah, M. (2017). Semi supervised semantic segmentation using generative adversarial network. In Proceedings of the IEEE International Conference on Computer Vision (pp. 5689–5697). https://doi.org/10.1109/ICCV.2017.606
Tang, W., Tan, S., Li, B., & Huang, J. (2017). Automatic steganographic distortion learning using a generative adversarial network. IEEE Signal Processing Letters, 24(10), 1547–1551. https://doi.org/10.1109/LSP.2017.2745572
Wang, P., Xiong, H., Fu, Y., & Li, X. (2019). Adversarial substructured representation learning for mobile user profiling. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 130–138). https://doi.org/10.1145/3292500.3330869
Wang, T. C., Liu M-Y, Zhu J-Y, Liu G., Tao A., Kautz J. & Catanzaro B. (2018). Video-to-video synthesis. In Advances in Neural Information Processing Systems (Vol. 2018-December, pp. 1144–1156). https://arxiv.org/abs/1808.06601v2
Wang, W. (2020). A reversible data hiding algorithm based on bidirectional difference expansion. Multimedia Tools and Applications, 79(9–10), 5965–5988. https://doi.org/10.1007/S11042-019-08255-Z
Wang, X., Chen, W., Wang, Y. F., & Wang, W. Y. (2018). No metrics are perfect: Adversarial reward learning for visual storytelling. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Vol. 1, pp. 899–909). https://doi.org/10.18653/v1/p18-1083
Wang X, Yu K., Shixiang W., Gu J., Liu Y., Dong C., Qiao Y. & Loy C.C. (2018). ESRGAN: Enhanced super-resolution generative adversarial networks. In Computer Vision – ECCV 2018 Workshops (pp. 63–79). Springer. https://doi.org/10.1007/978-3-030-11021-5_5
Wang, Z., & Zhang, X. (2018). Secure cover selection for steganography. IEEE Access, 7, 57857–57867. https://doi.org/10.1109/ACCESS.2019.2914226
Wang, Z., Feng, G., Shen, L., & Zhang, X. (2023). Cover selection for steganography using image similarity. IEEE Transactions on Dependable and Secure Computing, 20(3), 2328–2340. https://doi.org/10.1109/TDSC.2022.3181039
Wang, Z., Zhang, X., & Qian, Z. (2020). Practical cover selection for steganography. IEEE Signal Processing Letters, 27, 71–75. https://doi.org/10.1109/LSP.2019.2956416
Wu, H., Zhang, J., Zheng, S., & Huang, K. (2019). GP-GAN: Towards realistic high-resolution image blending. In Proceedings of the 27th ACM International Conference on Multimedia (pp. 2487–2495). https://doi.org/10.1145/3343031.3350944
Xie, X. Z., Chang, C. C., & Hu, Y. C. (2020). An adaptive reversible data hiding scheme based on prediction error histogram shifting by exploiting signed-digit representation. Multimedia Tools and Applications, 79(33–34), 24329–24346. https://doi.org/10.1007/S11042-019-08402-6
Yeh, R. A., Chen, C., Lim, T. Y., Schwing, A. G., Hasegawa-Johnson, M., & Do, M. N. (2017). Semantic image inpainting with deep generative models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 6882–6890). https://doi.org/10.1109/CVPR.2017.728
Yi, R., Liu, Y. J., Lai, Y. K., & Rosin, P. L. (2019). APDrawingGAN: Generating artistic portrait drawings from face photos with hierarchical GANs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10735–10744). https://doi.org/10.1109/CVPR.2019.01100
Yin, W., Fu, Y., Sigal, L., & Xue, X. (2017). Semi-latent GAN: Learning to generate and modify facial images from attributes. arXiv. https://arxiv.org/abs/1704.02166v1
Younus, Z. S., & Hussain, M. K. (2022). Image steganography using exploiting modification direction for compressed encrypted data. Journal of King Saud University - Computer and Information Sciences, 34(6), 2951–2963. https://doi.org/10.1016/J.JKSUCI.2019.04.008
Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., & Huang, T. S. (2018). Generative image inpainting with contextual attention. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5505–5514). https://doi.org/10.1109/CVPR.2018.00577
Zhang, S., Li, H., Li, L., Lu, J., & Zuo, Z. (2022). A high-capacity steganography algorithm based on adaptive frequency channel attention networks. Sensors, 22(20). https://doi.org/10.3390/S22207844
Zhang, K. A., Cuesta-Infante, A., Xu, L., & Veeramachaneni, K. (2019). SteganoGAN: High capacity image steganography with GANs. arXiv. https://doi.org/10.48550/arXiv.1901.03892
Zhanga, S., Xiaob, Y., Tianc, H., & Lid, X. (2025). A multi-image steganography: ISS. Cybersecurity, 8(1), 1–18. https://doi.org/10.1186/S42400-024-00333-6
Zhao, X., & Huang, H. (2021). Research on image steganography based on multiple expansion generation adversarial network. In 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC 2021) (pp. 361–366). https://doi.org/10.1109/ICFTIC54370.2021.9647204
Zhou, S., Xiao, T., Yang, Y., Feng, D., He, Q., & He, W. (2017). GeneGAN: Learning object transfiguration and attribute subspace from unpaired data. In British Machine Vision Conference 2017. https://doi.org/10.5244/C.31.111

Details

Primary Language

English

Subjects

Information Security and Cryptology

Journal Section

Research Article

Authors

Metin Eken ^*
0000-0002-2775-5230
Türkiye

İclal Çetin Taş
0000-0002-1101-9773
Türkiye

Publication Date

October 16, 2026

Submission Date

August 30, 2026

Acceptance Date

October 15, 2026

Published in Issue

Year 2026 Number: 2026

DOI

https://doi.org/10.53070/bbd.2027695

IZ

https://izlik.org/JA73PZ44MW

Cite

RIS / Bibtex

APA

Eken, M., & Çetin Taş, İ. (2026). Performance Evaluation of Steganogan Using Hybrid Image Datasets and Statistical Quality Metrics. Computer Science, 2026. https://doi.org/10.53070/bbd.2027695

AMA

1.Eken M, Çetin Taş İ. Performance Evaluation of Steganogan Using Hybrid Image Datasets and Statistical Quality Metrics. JCS. 2026;(2026). doi:10.53070/bbd.2027695

Chicago

Eken, Metin, and İclal Çetin Taş. 2026. “Performance Evaluation of Steganogan Using Hybrid Image Datasets and Statistical Quality Metrics”. Computer Science, nos. 2026. https://doi.org/10.53070/bbd.2027695.

EndNote

Eken M, Çetin Taş İ (October 1, 2026) Performance Evaluation of Steganogan Using Hybrid Image Datasets and Statistical Quality Metrics. Computer Science 2026

IEEE

[1]M. Eken and İ. Çetin Taş, “Performance Evaluation of Steganogan Using Hybrid Image Datasets and Statistical Quality Metrics”, JCS, no. 2026, Oct. 2026, doi: 10.53070/bbd.2027695.

ISNAD

Eken, Metin - Çetin Taş, İclal. “Performance Evaluation of Steganogan Using Hybrid Image Datasets and Statistical Quality Metrics”. Computer Science. 2026 (October 1, 2026). https://doi.org/10.53070/bbd.2027695.

JAMA

1.Eken M, Çetin Taş İ. Performance Evaluation of Steganogan Using Hybrid Image Datasets and Statistical Quality Metrics. JCS. 2026. doi:10.53070/bbd.2027695.

MLA

Eken, Metin, and İclal Çetin Taş. “Performance Evaluation of Steganogan Using Hybrid Image Datasets and Statistical Quality Metrics”. Computer Science, no. 2026, Oct. 2026, doi:10.53070/bbd.2027695.

Vancouver

1.Metin Eken, İclal Çetin Taş. Performance Evaluation of Steganogan Using Hybrid Image Datasets and Statistical Quality Metrics. JCS. 2026 Oct. 1;(2026). doi:10.53070/bbd.2027695

Publication Ethics and Publication Malpractice Statement

Journal of Computer Science (JCS) publishes original and unpublished papers in computer science. The responsibility of plagiarism is upon to all authors/corresponding author. We try to protect intellectual honesty actively in scientific area of our journal, and to prevent misconduct.
The publication process at JCS is the basis of the improvement and dissemination of information objectively and respectfully. In order to improve the quality of scientific studies, peer-reviewed studies are the first step of this aim. At this point, it is of utmost importance that all authors, researchers, publishers, reviewers, readers are included in the publication process; comply with the standards of ethical considerations. JCS expects all parties to hold the following ethical responsibilities.
The following ethical duties and responsibilities are written in the light of the guide and policies made by Committee on Publication Ethics (COPE).
The publication of an article in a peer-reviewed journal is an essential building block in the development of a coherent and respected network of knowledge. It is a direct reflection of the quality of the work of the authors and the institutions that support them. Peer-reviewed articles support and embody the scientific method. It is therefore important to agree upon standards of expected ethical behaviour for all parties involved in the act of publishing: the author, the journal editor, the peer reviewer, and the publisher.

Authorship and publication: Authors must send papers that do not contain
• plagiarism (taking the ideas and work of other researchers without citation is unfair and dishonest (indirected citation is also unfair and dishonest). Taking (copying) even one of your own that has previously been published, or even one sentence from someone else’s paper without proper citation is considered as plagiarism – please use your own words instead)
• fake data (the faked data (data fabrication) means the researchers did not actually do the study))
• data falsification (The researchers did the experiments, then changed portion of the data).
• Redundant publications (salami) (The editors should reject a weak paper that they suspect is a result of salami slicing).
Authors should specify list of references, financial support. It is forbidden to publish same research in more than one journal.

Responsibilities of being authors: All authors should have significant contribution in studies/researches. All data used in studies should be real and authentic (no data fabrication, no data falsification, etc.). Authors obliged to participate in peer review processes. All authors are obliged to provide retractions or corrections of mistakes. All authors have rights to use their studies and researches’ results. The detection of improper authorship in any paper concludes in paper rejection and institution informed. Publishing many very similar papers based on the same experiment is redundant publications, and forbidden (the sending same manuscript to more than one journal at the same time is an unehtical issue. We will have to be retracted the papers which were published in more than one journal).

Peer review / responsibility for the reviewers: Reviewers should make an objective decision about papers without any type influential. Reviewers should have no conflict of interest with respect to the research, the authors and/or the research funders. Reviewers should point out relevant published work which is not yet cited. Reviewing processes should be treated/kept confidentially.

Editorial responsibilities: Editors have complete responsibility and authority to reject/accept an article. Editors should have no conflict of interest with respect to articles they reject/accept. Only accept a paper when reasonably certain. When errors are found, promote publication of correction or retraction. Preserve anonymity of reviewers. Editors should take in care no plagiarism, no fraudulent data.

Publishing ethics issues: Editorial board monitors/safeguards publishing ethics by their responsibilities. Editorial specifies the guidelines for retracting papers (such as rejection, retracking and informing related institutes). Editorial board maintains the integrity of the academic record, and precludes business needs from compromising intellectual and ethical standards. Editorial board always be willing to publish corrections, clarifications, retractions and apologies when needed.

Duties of the Publisher: JCS is committed to ensuring that commercial revenue has no impact or influence on editorial decisions. In addition, JCS will assist in communications with other journals and/or publishers where this is useful to editors. Finally, we are working closely with other publishers and industry associations to set standards for best practices on ethical matters, errors and retractions–and are prepared to provide specialized legal review and counsel if necessary.

Plagiarism Detection: JCS uses double-blind review fulfilled by at least two reviewers. In addition, all articles are checked by means of similarity rate report supported by authors. Authors should use a program (ithenticate, turniton, etc) in order to confirm they are not published before and avoid plagiarism.
The peer review process is at the heart of the success of scientific publishing. As part of our commitment to the protection and enhancement of the peer review process, JCS has an obligation to assist the scientific community in all aspects of publishing ethics, especially in cases of (suspected) duplicate submission or plagiarism.
When a reader discovers a significant error or inaccuracy in a paper published in JCS or have any other complaint about editorial content (plagiarism, duplicate papers, etc.), he/she should make a complaint by e-mail to: ali.karci@inonu.edu.tr. We welcome complaints as they provide an opportunity for improvement, and we aim to respond quickly and constructively.

Research with Humans or Animals: Research involving humans or animals should be approved by relevant ethics committee(s) and should conform to international ethical and legal standards for research. We also expect authors to respect human participants’ right to privacy, and to gain any necessary consent to publish before submitting to JCS.
Competing Interests and Funding: We try to ensure that any JCS publication is free from undue influence. Authors submitting a journal manuscript to JCS, editors of JCS are required to declare any potential competing interests that could interfere with the objectivity or integrity of a publication. We also expect that anyone who suspects an undisclosed competing interests regarding a work published or under consideration by JCS should inform the relevant editor or email ali.karci@inonu.edu.tr.

Libel, Defamation and Freedom of Expression: Freedom of expression is critical to us as academic publishers, but we do not support publishing false statements that harm the reputation of individuals, groups, or organisations. Our legal team can advise on pre-publication libel reviews, and will also address allegations of libel in any of our publications.

Retractions, Corrections and Expressions of Concern: Journal editors will consider retractions, corrections or expressions of concern in line with COPE’s Retraction Guidelines. If an author is found to have made an error, the journal will issue a corrigendum. If the journal is found to have made an error, they will issue an erratum. Retractions are usually reserved for papers that are so seriously flawed that their findings or conclusions should not be relied upon, or that contain substantial plagiarism or life-endangering content. In exceptional cases, we may remove an paper from online publication where we believe it is necessary to comply with our legal obligations.

Image Manipulation, Falsification and Fabrication: Where research data are collected or presented as images, modifying these images can sometimes misrepresent the results obtained or their significance. We recognise that there can be legitimate reasons for modifying images, but we expect authors to avoid modifying images where this leads to the falsification, fabrication, or misrepresentation of their results.
Fraudulent Research and Research Misconduct: Where we are made aware of fraudulent research or research misconduct by a JCS author, our first concern is the integrity of content we have published. We work with the relevant editor(s), COPE, and other appropriate institutions or organisations, to investigate. Any publication found to include fraudulent results will be retracted, or an appropriate correction or expression of concern will be issued.

Versions and Adaptations: We neither modify existing, published content nor originate new materials to meet political or ideological requirements where we judge these to compromise the quality, effectiveness or factual accuracy of the materials or to conflict with our Code of Ethics.

Data and Supporting Evidence: We support transparency and openness around data, code, and other materials associated with research. We expect authors to maintain accurate records of supporting evidence necessary to allow others to understand, verify, and replicate new findings, and to supply or provide access to this supporting evidence, on reasonable request.

Fair Access: DergiPark ULAKBIM has an expansive developing web platform to allow free access to our digital content for researchers all around the world.

Publication Policy

Manuscripts submitted for our journal publishing program are initially reviewed by the editor(s). If the manuscript is suitable for consideration by Journal of Computer Science (JCS), the manuscript, along with its current content, will be sent to a minimum of two external and independent peer reviewers. The peer reviewers’ assessments are used to inform the editor’s decision as to whether or not to recommend publication to the JCS. Our editors are free to solicit additional reviews and guidance post-contract to inform the development of the manuscript.

GenAI: Papers produced with artificial intelligence are not published in this journal. Responsibility for articles produced with artificial intelligence belongs to the authors and when they are noticed, the publication process is cancelled. This situation is reported to the authors' institutions.

Editor in Chief

Ali KARCI

Information and Computing Sciences, Data Structures and Algorithms, Software Engineering (Other), Statistics, Engineering

Editorial Board

Taner UÇKAN

Information and Computing Sciences, Algorithms and Calculation Theory, Data Mining and Knowledge Discovery, Database Systems, Natural Language Processing, Computer Software

Ebubekir SEYYARER

Information and Computing Sciences, Information Systems, Image Processing, Algorithms and Calculation Theory, Machine Learning, Data Mining and Knowledge Discovery, Database Systems, Artificial Intelligence, Computer Software, Programming Languages

ŞK

Şeyda KARCI ŞEN

Image Processing, Data Structures and Algorithms

Cengiz HARK

Information and Computing Sciences, Deep Learning, Natural Language Processing

AA

Abdullah ATEŞ

Digital Processor Architectures, Optimization in Manufacturing

Ahmet KARADOĞAN

Computer Software

Kazım HANBAY

Image Processing, Pattern Recognition, Machine Vision

Mücahit SOYLU

Information Security Management, Algorithms and Calculation Theory, Numerical Computation and Mathematical Software, Information Visualisation, Computer System Software, Software Engineering (Other)

Ahmet Arif AYDIN

Machine Learning Algorithms, Big Data, Database Systems, Computer Software

FÖ

Furkan ÖZTEMIZ

Algorithms and Calculation Theory, Big Data, Data Mining and Knowledge Discovery, Database Systems, Computer Software

Kİ

Kenan İNCE

Information and Computing Sciences, Information Security and Cryptology, Cryptography

Murat AYDOĞAN

Machine Learning, Deep Learning, Information Security and Cryptology, Big Data, Natural Language Processing

Muhammed TALO

Machine Learning, Machine Vision

SY

Selman YAKUT

Algorithms and Calculation Theory, Information Security and Cryptology, Cryptography, Data and Information Privacy, Computer Software

Ali KARCI

Information and Computing Sciences, Data Structures and Algorithms, Software Engineering (Other), Statistics, Engineering

Teymuraz ABBASOV

Electrostatics and Electrodynamics, Power Electronics

Adnan Fatih KOCAMAZ

Quantum Computation, Intelligent Robotics, Quantum Computers

ÖA

Ömer Faruk ALÇIN

Deep Learning, Biomedical and Clinical Sciences, Signal Processing

FO

Fatih OKUMUŞ

Information and Computing Sciences, Information Systems Development Methodologies and Practice, Information Systems User Experience Design and Development, Business Process Management, Image Processing, Pattern Recognition, Cloud Computing, Algorithms and Calculation Theory, Human-Computer Interaction, Big Data, Data Mining and Knowledge Discovery, Database Systems, Artificial Intelligence, Intelligent Robotics, Empirical Software Engineering, Computer Software, Engineering, Mechatronics Engineering

AK

Ayla KAYABAŞ

Deep Learning, Natural Language Processing

MK

Meral KARAKURT

Deep Learning, Neural Networks, Supervised Learning, Machine Learning Algorithms, Classification Algorithms, Machine Learning (Other), Satisfiability and Optimisation

MT

M. Emin TAĞLUK

Image Processing, Deep Learning, Neural Networks, Extreme Learning Machines, Classification Algorithms, Bioinformatics, Data Mining and Knowledge Discovery, Autonomous Agents and Multiagent Systems, Artificial Life and Complex Adaptive Systems, Engineering, Biomedical Sciences and Technology, Electrical Engineering, Signal Processing