arXiv:2605.20971v1 Announce Type: cross Abstract: The growing sophistication of GAN-based image manipulation presents significant challenges for digital forensics. This study compares the performance of four pretrained CNN architectures including VGG16, ResNet50, EfficientNetB0, and XceptionNet for fake image detection using a unified preprocessing and training pipeline. A dataset of real and manipulated images was processed through resizing, normalization, and augmentation to address class imbalance and improve generalization. Models were evaluated using Accuracy, Precision, Recall, F1-score,
The rapid advancement and accessibility of sophisticated image generation models, particularly GANs, necessitate immediate countermeasures for digital trust and forensics. This research directly addresses the growing challenge of distinguishing real from synthetically generated content.
The proliferation of fake images poses significant risks to information integrity, public trust, and democratic processes, making robust detection methods crucial for national security and societal stability. This evaluation provides critical insights into the efficacy of current deep learning approaches.
This research provides a comparative benchmark for deep learning models in fake image detection, offering a more informed basis for selecting and deploying defensive technologies. It highlights the improving capabilities of AI in counter-AI applications.
- · Digital forensics companies
- · Social media platforms
- · Security software developers
- · Information integrity initiatives
- · Malicious actors using deepfakes
- · Disinformation campaigns
- · Unprepared news organizations
Improved detection capabilities will reduce the impact of fake images on public perception and decision-making.
An arms race between generative AI and detection AI will accelerate, driving further innovation in both fields.
The development of real-time, on-device fake image detection could become a standard feature in digital communication, altering how information is consumed and trusted.
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Read at arXiv cs.AI