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    Investigation of steganalysis performance of selected steganography methods with deep learning models
    (Gazi Univ, Fac Engineering Architecture, 2024) Bulus, Ercan
    Purpose:It is to research whether steganalysis can be done with deep learning methods. Theory and Methods: 10,000 BOWS 2 and 4,000 BOSSBase 1.01 pairs were used for training, 1,000 BOSSBase 1.01 pairs forvalidation, and 5,000 BOSSBase 1.01 pairs for testing. HILL, MiPOD, S-UNIWARD and WOW steganography methods were used to information hiding. Steganalysis was performed with VGG16, Xu-Net, Ye-Net and Yeroudj-Net Deep learning models. Results: At 0.2 bpp load there is not much difference between the results and not quite as expected. The best result in wow at 0.4 payload was obtained with the VGG16 (86.4%) method. However, VGG16 test times are twice as long compared to others. In this case, the Yedroudj method seems more suitable in terms of accuracyrate and processing time. Conclusion: When the test accuracy results for 0.2bpp load are examined in the study; It is seen that the lowest result is 50% (0.500) in the YEDROUDJ model with the MIPOD method, and the most efficient result is 69% (0.69) in the XU-net model for all methods. Since acceptable results could not be achieved with the VGG16 model under 0.2bpp load, it was not evaluated. In this case, the Xu-net model can be preferred to investigate whetherinformation is hidden in images with a low amount of information such as 0.2bpp. Again, when the test accuracy results for 0.4bpp load are examined in the study; It is seen that the lowest result is obtained withthe Xu-net model in the MIPOD method with 72% (0.72) and again with the Ye-Net model in the MIPOD method with 72% (0.72). On the other hand, the highest result was obtained with the VGG16 model in the WOW method with 86% (0.864) and with the VGG16 model with 84% (0.847). However, when the processing times in Table 11 are examined, it is clear that the processing time of the examinations made with the VGG16 model is almost twice as long as the others, and this is a very undesirable situation. In this case, the Yedroudj method is seen as a more useful method since it has the second best accuracy rate between 75%-82% with 0.4bpp load and a lower processing time (half) than VGG16.
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    Mechanical properties of Sr inoculated A356 alloy by Taguchi-based gray relational analysis
    (Walter De Gruyter Gmbh, 2024) Yilmaz, Serdar Osman; Teker, Tanju; Dalmis, Ibrahim Savas; Bulus, Ercan
    In this study, Sr inoculated A356 alloy casted by sand-casting technique. Production parameters such as Sr concentration (wt.%), aging temperature (degrees C), aging time (h), and constant cooling rate were used. The effect of heat treatment on the microstructure and mechanical features of inoculated A356 materials was examined by using scanning electron microscopy, optical microscopy, and the Taguchi-based gray relational analysis method. The optimum production parameters for A356 alloy were determined as 0.03 Sr concentration, aging 300 degrees C temperature, and 3 h aging time. Multiple response optimization based on the interaction of these parameters provided a 30.15 % improvement in performance. Gray relational grade (GRG) experimental results showed that the most important parameter was Sr concentration, with a contribution of 76.51 %, according to the analysis by ANOVA statistical method.