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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Ronaldus Morgan James
Institut Teknologi dan Bisnis Sabda Setia
Computer Science & IT Engineering

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ANALISIS KOMPARATIF ARSITEKTUR XCEPTIONNET DAN EFFICIENTNETB0 UNTUK ATRIBUSI PROVENANCE CITRA DEEPFAKE MULTI-KELAS: COMPARATIVE ANALYSIS OF XCEPTIONNET AND EFFICIENTNETB0 ARCHITECTURES FOR MULTI-CLASS DEEPFAKE IMAGE PROVENANCE ATTRIBUTION Ronaldus Morgan James
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7104

Abstract

The rapid evolution of generative artificial intelligence (AI) models such as DALL·E, Midjourney, and Stable Diffusion has intensified the risk of visual disinformation, as synthetic images increasingly resemble real ones. Traditional binary detection methods (real vs. fake) have become insufficient, creating a growing need for provenance attribution, i.e., identifying the specific generative model responsible for producing an image. This study presents a comparative evaluation of two Convolutional Neural Network (CNN) architectures—XceptionNet and EfficientNetB0—for multi-class attribution of synthetic images. The primary objective is to evaluate and compare the effectiveness of both architectures in attributing images to four specific source classes: DALL·E, Midjourney, Stable Diffusion, and real images. Both models were trained and tested using a transfer learning approach on a balanced dataset of 2,000 samples and assessed using accuracy, precision, recall, and F1-score. Experimental results show that EfficientNetB0 outperforms XceptionNet, achieving 95.2% accuracy compared to 93.5%, while also exhibiting more stable training behavior and stronger discriminative capability for visually similar classes. The findings indicate that EfficientNetB0 offers a more reliable balance of computational efficiency and feature extraction performance, making it a suitable architecture for provenance attribution tasks involving generative AI imagery.  
ANALISIS PERBANDINGAN KETANGGUHAN RESNET50 DAN EFFICIENTNETB0 TERHADAP DEGRADASI GAUSSIAN BLUR PADA ATRIBUSI PROVENANCE DEEPFAKE Ronaldus Morgan James
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8306

Abstract

The rapid advancement of generative artificial intelligence models has escalated the risks of deepfake image dissemination, necessitating a paradigm shift from binary detection to provenance attribution, i.e., identifying the specific AI source model. Although various Convolutional Neural Network (CNN) architectures have proven highly accurate in classifying image sources under ideal conditions, real-world implementations frequently encounter spatial degradations, such as blurring effects, which destroy AI-generated high-frequency artifacts. This study presents a comparative analysis of the robustness of ResNet50 and EfficientNetB0 architectures in attributing multi-class deepfake images (DALL-E, Midjourney, Stable Diffusion, and Real Images) distorted by Gaussian Blur attacks, using a transfer learning approach to evaluate the degradation rate of both models. Experimental results indicate that while EfficientNetB0 achieved an accuracy of 93.89% under ideal conditions, its performance dropped significantly by 6.56% to 87.33% when images were blurred, due to the sensitivity of its depthwise convolution layers. In contrast, ResNet50, which achieved 95.70% accuracy under ideal conditions, demonstrated far superior robustness with only a 1.58% accuracy degradation, retaining 94.12% accuracy after the Gaussian Blur attack. This advantage is empirically validated as the effect of skip connections in the residual network, which preserve the spatial integrity of semantic feature representations. This study recommends the use of residual-based architectures for digital forensic systems operating in real-world environments that are susceptible to compression and degradation.
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