Jul 2026· International Journal of Innovative Science and Research Technology· 0 citations· 8 references
TL;DR
Experimental results demonstrate that the proposed approach effectively identifies deepfake images with high accuracy, making it suitable for applications in digital forensics, media verification, and cybersecurity.
Abstract
The rapid advancement of generative artificial intelligence has significantly increased the creation of highly
realistic manipulated images, commonly known as deepfakes. These synthetic images pose serious threats to digital
security, privacy, and information authenticity, as they are often indistinguishable from real images through human
observation. This growing challenge necessitates the development of automated and reliable detection systems capable of
identifying subtle visual inconsistencies in manipulated content.This paper presents a deep learning–based approach for
detecting deepfake images using a transfer learning framework. A pretrained convolutional neural network model,
MobileNetV2/EfficientNet, is utilized for feature extraction, followed by classification layers to distinguish between real
and fake images. The proposed system incorporates image preprocessing techniques such as resizing, normalization, and
data augmentation to improve model robustness. The model is trained on benchmark datasets containing both genuine
and manipulated images and evaluated using performance metrics such as accuracy, precision, recall, and F1-score.
Experimental results demonstrate that the proposed approach effectively identifies deepfake images with high accuracy,
making it suitable for applications in digital forensics, media verification, and cybersecurity.
The rapid growth of artificial intelligence has led to the development of advanced technologies capable of generating highly realistic fake images and videos, commonly known as deepfakes. Although these technologies have useful applications in entertainment, education, and digital media, they can also be misused for spreading misinformation, identity theft, fraud, and other cybercrimes. This paper presents an AI-based fake image and video detection system using deep learning models such as InceptionV3, EfficientNet, and a Hybrid model. The proposed system preprocesses input images and video frames through resizing, normalization, and data augmentation to improve model performance. The trained models extract meaningful visual features and classify the uploaded media as real or fake. The Hybrid model combines the strengths of InceptionV3 and EfficientNet to achieve improved feature extraction and higher classification accuracy. The performance of the proposed system is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the Hybrid model outperforms the individual models in detecting manipulated media. The proposed approach provides an efficient, accurate, and reliable solution for identifying AI-generated content and helps improve the authenticity, security, and trustworthiness of digital media across various online platforms.
Vidyashree H R and Dr. Manjunath B· International Journal of Adv...· 0 citations
The rapid progress in artificial intelligence (AI), machine learning, and deep learning has led to the development of innovative tools for multimedia content manipulation. While these technologies have legitimate applications in entertainment and education, they have also been misused to create Deep fakes highly realistic fake videos, images, and audio. Deep fakes are often exploited to spread misinformation, propaganda, and political discord, as well as for harassment and blackmail. In response, researchers have developed various detection techniques, including machine learning, spectral analysis, steganography, and feature extraction. This paper presents a systematic literature review of 56 relevant studies published between 2007 and early 2025, analyzing different forgery detection methods. The review highlights the effectiveness of deep learning-based approaches, which excel due to their ability to process large and complex datasets, perform end-to-end learning, adapt to new challenges, and offer robustness, scalability, and continuous improvements. Additionally, widely used benchmark datasets such as Face Forensics++ and Celeb-DF have played a crucial role in advancing research in this domain. Standardized evaluation metrics ensure consistency in model comparisons and facilitate systematic progress tracking. This comprehensive review underscores ongoing efforts to combat image manipulation and safeguard the authenticity of visual media.
MALGWI, YUSUF MUSA, ELKANAH, ADATI CHAHARI, MOHAMMED, ADAU LIMAN et al.· International Journal of Con...· 0 citations
This review presents a comprehensive analysis of recent deep learning and transfer learning techniques for fake image detection, examining widely adopted convolutional neural network architectures, benchmark datasets, evaluation metrics, and current research developments.
Nisha Parveen, Anjali Saxena· International Journal for Re...· 0 citations
This project presents an explainable deep learning framework for identifying real and AI-generated images using the NASNet architecture and achieves high detection accuracy while providing interpretable visual explanations, making it suitable for digital image verification, media authentication, and cybersecurity applications.
Panduga Mounika, Dr.CH. Buchi Reddy· American Journal of AI Cyber...· 0 citations
The increasing accessibility of digital image editing tools has made image manipulation easier than ever before, creating significant challenges in verifying the authenticity of digital visual content. Copy-move forgery is one of the most widely used image manipulation techniques because duplicated regions originate from the same image, making manual detection difficult. Existing forgery detection approaches often rely on handcrafted feature extraction or a single deep learning architecture, which may exhibit reduced performance when images undergo transformations such as rotation, scaling, compression, illumination variation, and noise addition. This paper presents a fusion-based lightweight deep learning framework for copy-move image forgery detection and localization. The proposed system integrates three efficient convolutional neural network architectures, namely SqueezeNet, MobileNetV2, and ShuffleNet, to extract complementary deep image features. Instead of depending on a single classifier, the extracted features are fused and classified using a Support Vector Machine (SVM), resulting in improved prediction reliability and reduced false detection. To provide visual evidence of manipulation, the framework incorporates a Discrete Cosine Transform (DCT)-based localization module that identifies duplicated image regions and highlights forged areas. The proposed model was implemented using Python with TensorFlow, OpenCV, Scikit-learn, NumPy, and Tkinter. Experimental evaluation was conducted on the MICC-F220 benchmark dataset. The fusion strategy achieved an overall detection accuracy of 90.91%, outperforming individual lightweight convolutional neural network models while maintaining low computational complexity. The proposed framework offers an efficient and practical solution for digital image authentication and is applicable to digital forensics, journalism, law enforcement, cyber security, and multimedia content verification.
K. Sumalini, K. B. Maruthiram· International Journal of Res...· 0 citations
Noise residual fingerprints are extracted by a simple yet effective pre-trained Noiseprint++ model, outperforming the state-of-the-art detectors on generalization ability, and the effectiveness of each module is validated by ablation studies.
Caihui Yan, Gang Cao, Huawei Tian et al.· arXiv.org· 0 citations
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