چکیده مقاله
The widespread utilization of Artificial Intelligence AI models, such as Generative AdversarialNetworks GANs , has demonstrated remarkable achievements in the field of image synthesis The proliferation ofAI generated images, created through GANs, has become prevalent on the Internet due to advancements ingenerating realistic and lifelike visuals While this development has the potential to enhance content and media, italso poses threats in terms of legitimacy, authenticity, and security Consequently, it is crucial to develop anautomated system capable of identifying and distinguishing between GAN generated images and real ones, servingas an evaluation tool for image synthesis models, regardless of the input modality To address this issue, we proposea framework that utilizes Convolutional Neural Networks CNNs to reliably detect AI generated images fromauthentic ones Initially, we collected a diverse set of GAN generated images from various tasks and architectures toensure the model's generalizability Subsequently, transfer learning was implemented, followed by the integration ofseveral Class Activation Maps CAM to identify the discriminative regions that guide the classification model inmaking decisions Our approach achieved a 100% accuracy on our dataset, which consisted of Real or SyntheticImages RSI , and demonstrated superior performance on other datasets and configurations Thus, our frameworkcan serve as an effective evaluation tool for image generation Our most successful detector was an EfficientNetB4model, pre trained on our dataset, fine tuned with a batch size of 64 and an initial learning rate of 0 001 for 20epochs We utilized the Adam optimizer and incorporated learning rate reduction techniques and data augmentationto further improve performance
کلیدواژهها
نویسندگان
AmirAbbas Ranjbar
Alireza ChamkooriReza MashayekhiPeyman ArebiSajed MohisenKarim Dameshgh
Alireza Chamkoori
AmirAbbas RanjbarReza MashayekhiPeyman ArebiSajed MohisenKarim Dameshgh
Reza Mashayekhi
AmirAbbas RanjbarAlireza ChamkooriPeyman ArebiSajed MohisenKarim Dameshgh
Peyman Arebi
AmirAbbas RanjbarAlireza ChamkooriReza MashayekhiSajed MohisenKarim Dameshgh
Sajed Mohisen
AmirAbbas RanjbarAlireza ChamkooriReza MashayekhiPeyman ArebiKarim Dameshgh
Karim Dameshgh
AmirAbbas RanjbarAlireza ChamkooriReza MashayekhiPeyman ArebiSajed Mohisen
شیوه ارجاع
Ranjbar, AmirAbbas and Chamkoori, Alireza and Mashayekhi, Reza and Arebi, Peyman and Mohisen, Sajed and Dameshgh, Karim,1402,Checking The Authenticity and Security of Files and Images ProducedBased on Artificial Intelligence Models,21st National Conference of Computer Science and Engineering and Information Technology,Babol
ارائهشده در
مجموعه مقالات بیست و یکمین کنفرانس ملی علوم و مهندسی کامپیوتر و فناوری اطلاعات30 آذر 1402 · بابل