Detection of LSB-Based Steganographic Data in Image Files Using Deep Learning Models

Öz

In this study, the detectability of data hidden with an LSB-based steganography method was examined using deep learning models. For the analysis, a stego dataset presented in the literature was used, and the results of experiments conducted at different hiding ratios (hr), bits per pixel values (hb), and image resolutions were evaluated using CNN-based models (ResNet-50, VGG, DenseNet, EfficientNet, MobileNet) and transformer-based models (ViT, DeiT, Swin, BEiT). In addition, a hybrid model was proposed in order to compare the results, and its outcomes were discussed. The results showed that at low hiding ratios the performances of all models were close, while at high hiding ratios the distortions became more visible and the discrimination ability increased. Among the CNN architectures, ResNet-50 achieved the highest performance with an average F1 score of 0.88. In the transformer group, Swin Transformer stood out in all scenarios and reached the highest F1 score of 0.91. In contrast, BEiT showed lower performance than the other transformer architectures at both low and high hiding ratios and obtained an average F1 score of 0.75. The findings indicate that both transformer- and CNN-based approaches can be used effectively for steganalysis, while the general trend shows higher performance and representation capability for transformer architectures, although some CNN models produced comparable results.

Anahtar Kelimeler

• CNN
• steganography
• steganalysis
• transformer

Destekleyen Kurum

Trakya University Scientific Research Projects Coordination Unit

Proje Numarası

2024/196

Resim Dosyalarında LSB Yöntemi ile Gizlenen Steganografik Verilerin Derin Öğrenme Modelleri Kullanarak Tespit Edilmesi

Öz

Bu çalışmada, LSB tabanlı steganografi yöntemi ile gizlenmiş verilerin tespit edilebilirliği derin öğrenme modelleri kullanılarak incelenmiştir. İnceleme için literatürde sunulan stego veri seti kullanılmış ve Evrişimsel Sinir Ağı (CNN) tabanlı ResNet-50, VGG, DenseNet, EfficientNet, MobileNet ve transformer tabanlı ViT, DeiT, Swin, BEiT modelleri kullanılarak farklı gizleme oranları, piksel başına bit sayıları ve çözünürlüklerde gerçekleştirilen deneylerden elde edilen sonuçlar değerlendirilmiştir. Ayrıca sonuçların karşılaştırılması amacıyla bir hibrit model önerisi sunulmuş ve sonuçlar irdelenmiştir. Değerlendirme sonuçları incelendiğinde düşük gizleme oranlarında tüm modellerin performanslarının birbirine yakın olduğu, yüksek gizleme oranlarında ise bozulmaların daha belirgin hale gelmesiyle ayrım gücünün arttığı gözlenmiştir. CNN mimarileri içerisinde ResNet-50 ortalama 0,88 F1 skoru ile en yüksek başarıyı sergilerken, transformer grubunda Swin Transformer tüm senaryolarda öne çıkmış ve en yüksek F1 skoru olan 0,91 değerine ulaşmıştır. Buna karşılık BEiT modeli hem düşük hem de yüksek gizleme oranlarında diğer transformer mimarilerinden düşük performans göstermiştir ve ortalama 0,75 F1 skoru elde etmiştir. Elde edilen bulgular, steganaliz görevinde hem transformer hem de CNN tabanlı yaklaşımların etkili biçimde kullanılabildiğini ortaya koymaktadır. Bununla birlikte, genel eğilim transformer tabanlı mimarilerin CNN’lere kıyasla daha yüksek performans ve temsil gücü sunduğunu göstermektedir. Öte yandan, bazı CNN mimarilerinin de transformer mimarileri kadar başarılı sonuçlar ürettiği görülmüştür.

Anahtar Kelimeler

• CNN
• steganografi
• steganaliz
• transformer

Destekleyen Kurum

Trakya Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü

Proje Numarası

2024/196

Kaynakça

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