In today’s rapidly evolving digital content ecosystem, the internet is flooded with millions of videos and images each day [1], some of which are tampered with using deepfake techniques. Deepfakes are fake/altered videos that are created with the help of AI tools and deep learning (DL) techniques [2,3]. These technologies can realistically recreate a person’s face and expression so that it becomes hard to judge whether the content is real or fake. This represents a major problem in every field, including politics, journalism, and social media. [4]. For example, a fake video can be generated using AI tools to make it appear as if a politician said something that they never said, or to show a celebrity performing something that they never did. The video will quickly become viral due to the confusion, spreading false information and damaging public opinion. One of the major concerns regarding fake videos is that they destroy the credibility of social media and public trust [5].

The loss of trust in social media platforms can have a serious effect on society, including political instability, online harassment, financial fraud, and blackmailing. Deepfakes are becoming more advanced day by day, and therefore, detection becomes more difficult. There is a strong need for an automatic system that can detect these fake videos quickly and accurately. This study focuses on such a system, which builds on DL techniques. The purpose is to develop a model for a tool that analyzes videos, helping individuals, along with organizations and media outlets, identify fake content before it spreads. There is a need for effective techniques to identify and deal with these manipulated videos.

With the massive growth of knowledge regarding deepfakes, researchers have created numerous algorithms and tools that are in development [6,7]. These tools are useful for identifying deepfakes, but they have certain gray areas that limit their usefulness. There are some web applications available, such as Deep Fake Detector, Detect Deepfakes, and Alterations. These tools help us to detect whether uploaded videos/movies are real or fake.

While these tools do help in some cases, they nonetheless fail to address important issues like ethical concerns, legal challenges, and public awareness. The above factors are very important when it comes to dealing with the harmful effects of fake videos on society. DeepFakeDetector (https://sightengine.com/detect-deepfakes) has a major limitation, which is that it only deals with analyzing videos of up to 5 megabytes (MB) in size. This is a major issue because in the modern era of digital content, videos are larger in size and of higher quality. As a result, this tool does not fulfill the requirements of the currently uploaded content shared on social platforms. Deepfake videos are becoming more common and more realistic, which makes it difficult to identify whether videos are original or fake [8]. Numerous researchers around the world have proposed various techniques and models to detect fake videos [9].

The following few paragraphs discuss how deepfake detection models work, what kind of datasets they use, how accurate they are, and what limitations they have. This section also provides detail information on the datasets that these researchers have used, such as FaceForensics++, DFDC, and Celeb-DF [10]. Datasets have been used widely in the field and contain both real and fake videos for training and testing models.

Classification plays an important role in deepfake image/video detection [11,12]. After the extraction of features from the video (like facial movements, eye blinking, head motion, or mouth movement), we need a model that can classify them into one of two categories: real or fake. Most researchers prefer to use DL models for this classification task due to their deep learning capabilities. The deep Convolutional Neural Network model, which is used for image classification, is known for its high number of layers and its architecture. Numerous researchers have combined both CNN and Long Short-Term Memory (LSTM) to take advantage of both image-based and time-based features. The CNN extracts features from each of the frames, and the LSTM analyzes the sequence of these features, in order to make the final decision at the end.

In addition to these approaches, some studies use transfer learning with pre-trained models like the Visual Geometric Group-16 (VGG-16), Residual Network 50 (ResNet-50), LSTM, Residual Networks with Aggregated Transformations, and EfficientNet (ResNeXt) [13]. These models are pre-trained on large image datasets, and they can quickly learn new tasks with less training data. Using the technique of transfer learning has sped up the development and improved the accuracy of these models. Although these DL techniques are powerful and have achieved high accuracy, they also present some challenges. They usually require high-performance hardware such as a Graphics Processing Unit or Tensor Processing Unit for the processing of large amounts of data. Also, running them in real-time environments, such as live video detection, can be slow or inefficient [14,15]. In conclusion, classification techniques are always the key part of any deepfake detection system.

Aarti Karandikar et al. [16] proposed a deepfake detection method that makes use of a transfer learning-enhanced VGG-16 CNN. This technique finds visual irregularities, especially between facial features, in order to identify deepfakes. This system had trouble with low-resolution deepfakes and was sensitive to compression artifacts. Similarly, Hanqing Zhao et al. [17] presented a Multilayer Feedforward Neural Network that can record artifacts at different levels of granularity. The model performed well, particularly when it came to identifying low-quality deepfakes. However, the model’s high energy and computing costs made real-time adoption challenging. TensorFlow and Keras were used to assess the approach on the FaceForensics++ dataset.

Yash Doke et al. [18] created a hybrid model that uses LSTM to capture temporal discrepancies and ResNetCNN to extract features. By concentrating on facial and kinematic abnormalities, this approach produced high detection accuracy and real-time performance. Its significant processing burden during frame-level analysis was its primary flaw. The model was developed using Python frameworks and trained on FaceForensics++ with a 70% training set and 30% test set. Alakananda Mitra et al. [19] created a deepfake detection model using a CNN combined with a classifier. Despite achieving high precision scores, the model was not resistant to different video quality and compression levels. It was trained using the FaceForensics++ dataset and implemented with Keras and TensorFlow.

Nicolo Bonettini et al. [20] suggested a hybrid approach to identify facial changes in deepfake films by fusing DL methods with conventional computer graphics. Although the CNN-based system was quite accurate, its real-time application was limited by modest processing delays. The FaceForensics++ and Deepfake Detection Challenge (DFDC) datasets in PyTorch were also used to assess the model. Ankur Nagulwar et al. [21] used Multi-task Cascaded Convolutional Networks (MTCNNs) to identify irregularities in noise and face features in video frames. The system successfully authenticated videos with single faces and achieved 70% accuracy. But when there were several faces in a single shot, its performance degraded. The FaceForensics++ dataset, which consists of films that have been transformed into frames for analysis, was used for the evaluation. Nimitt Patel et al. [22] used an LSTM and ResNext CNN to identify video frames in a deepfake detection model. However, the model’s usefulness was limited due to the limited visual content examined, and it did not analyze audio. The preprocessing with cropping and resizing, FaceForensics++, was used for training.

Neeraj Guhagarkar et al. [23] provided a thorough analysis of several AI and machine learning-based deepfake detection techniques. They examined temporal and spatial approaches and highlighted high-performing designs, such as CNN+LSTM. The study identified issues with low-quality movies and real-time identification despite the extensive use of datasets such as Celeb-DF, University of Albany Deep Fake Video (UADFV), and Reddit Deepfakes. TensorFlow, Xception, and Visual Geometry Group (VGG) were commonly used tools. Ahmed Hatem Soudy et al. [24] suggested a hybrid deepfake detection system that uses a majority voting method and combines CNNs with vision transformers. The eyes, nose, and entire face were the focal points of their model. Although it needed a lot of processing power and might have missed face alterations in other regions, it was able to reach up to 80% accuracy. The FaceForensics++ and DFDC datasets were used to train and assess the system.

Similarly, Arash Heidari et al. [25] carried out a study of the literature on DL-based deepfake detection techniques. CNNs, GANs, RCNNs, and other models for image, video, and audio detection were discussed in the study. Methods outside of DL were not included; they were grouped according to performance and application. They concentrated on models built using platforms like TensorFlow and Keras and compared them using datasets including CelebA, TIMIT, and DFDC. A detailed yet comprehensive literature review is presented in Table 1.

When a fake video, which is generated very carefully and with advanced tools, looks very realistic, these tools are unable to detect the changes made in the video properly. This also leads to inaccurate results, especially when it comes to detecting fake videos that are professionally made/edited. The limitations clearly show the need for a more advanced and more reliable fake video detection system, which can not only process large and high-quality videos but can also provide accurate and trustworthy results. The system should be user-friendly and should be capable of helping media agencies and legal authorities to identify fake content effectively. This study aims to fill the gaps in the research by developing a more efficient and powerful fake video detection tool to overcome these challenges.

The accuracy and dependability of current detection techniques are sometimes insufficient to handle the increasing difficulty of identifying manipulated material, especially deepfakes. Due to this restriction, it is challenging to identify small alterations in video content, which enables bogus videos to aid in the dissemination of false information. The social, political, and media landscapes are at serious risk, and public confidence in digital media is eroded as a result.

The following are the aims and objectives of the study:

This study addresses the issues and challenges that offer reliable and user-friendly problem solutions for verifying whether a video is authentic or not. The system is built to adapt to all these limitations and provide accurate and high-quality results. By using the advanced DL model and user-friendly web interface, the tool can be used for detecting deepfake videos. In addition, we also compared the accuracy of each method to understand how well it performs. Some of the models must achieve high accuracy but may be limited to a specific type of fake video, or they might not perform as well as they are expected to in real-world scenarios where lighting and quality vary. Through this research, by reviewing all these models, datasets, and results, we gain a better understanding of what works perfectly and what does not.

This study focuses on detecting fake videos, also known as deepfakes, using DL techniques. The methodology includes several key steps: dataset selection, preprocessing, model training, and evaluation, as shown in Figure 1. Each of these steps plays a vital role in ensuring that the model accurately distinguishes real from fake videos. The proposed system is designed for the detection of fake videos using a DL-based approach.

The architecture shown in Figure 2 includes several components, such as the selection of the dataset, preprocessing of the dataset, selection of the model, model training, video analysis, and the display of the results on the UI. The DL model is trained and evaluated for fake video detection.

The Google Colab environment’s TensorFlow and Keras frameworks were used for the training procedure. A batch size of 32 samples was used to train the system across 30 epochs. Model weights were updated during training using the Adam optimizer, which was chosen due to its effectiveness while working with sparse gradients and large datasets. During training, a constant learning rate of 0.001 was used. The binary cross-entropy loss function was employed because it works well for two-class classification issues. In general, the training setup was chosen to minimize overfitting and provide strong convergence.

Figure 3 demonstrates the model’s ability to discriminate between real and false frames, as shown by the high number of true positives and true negatives. There were a few instances of false positives, in which genuine frames were mistakenly identified as fraudulent.

The confusion matrix presented in Figure 3 illustrates the classification performance of the proposed binary classification model in distinguishing between real and fake frames. The matrix indicates that the model correctly classified 20,092 real samples as real and 20,453 fake samples as fake, resulting in a total of 40,545 correct predictions out of 49,246 test samples. Conversely, 6213 real samples were incorrectly classified as fake, while 2488 fake samples were misclassified as real. These results correspond to an overall classification accuracy of approximately 82.33%, demonstrating that the proposed model effectively differentiates between the two classes while maintaining a relatively low misclassification rate.

These results suggest that the model is more conservative in predicting the real class, thereby reducing the chances of fake samples being accepted as real. Although some genuine samples are falsely identified as fake, the overall distribution of correct predictions demonstrates that the classifier is well-balanced and reliable for practical applications. Figure 3 confirms the robustness of the proposed model and highlights its effectiveness in accurately recognizing both classes while providing opportunities for further optimization to reduce false classifications, particularly for the real class.

The following Figure 4 shows the evaluation metrics of the deepfake detector. These metrics show in detail the classification ability of the model on the test set and allow us to determine how well the system can distinguish between real and fake videos. A high precision value indicates that most of the videos classified as fake are fake, reducing the false positive cases, while a high recall value indicates that many of the fake videos are properly identified by the model, therefore reducing the cases of false negatives. F1-score, as the harmonic mean of precision and recall, reveals the similarity in the results of those two measures, as well as the reliability of the grouping model in terms of the two classes. Lastly, there is the overall accuracy of the model with reference to the accurate classification of samples on the test set.

The following Figure 5 displays the model’s training and validation losses over 30 epochs. As can be observed, the training loss gradually drops, suggesting that the model is successfully picking up on trends in the data. The model appears to be generalizing well with new data, as evidenced by the validation loss’s comparable trend and its close alignment with the training curve. One indication that there is little overfitting is the lack of significant gaps or oscillations between the two curves.

Similarly, Figure 6 below further illustrates the training and validation accuracy of the proposed ResNet-50 model over 27 training epochs. The graph demonstrates a steady improvement in classification performance throughout the training process, indicating that the model effectively learns meaningful feature representations from the training data. The training accuracy increases consistently from approximately 53% in the first epoch to 80% in the final epoch, reflecting gradual convergence and stable optimization. Similarly, the validation accuracy improves from approximately 56% to 83%, showing that the model generalizes well to previously unseen data.

It should be noted that the validation accuracy remains slightly higher than the training accuracy during most training epochs. This behavior suggests that the model benefits from effective regularization techniques, such as data augmentation, dropout, or batch normalization, which help prevent overfitting and improve generalization performance. Moreover, the relatively small gap between the two curves indicates that the model has achieved a good balance between learning the training data and maintaining strong predictive performance on the validation set.

A steady rising trend is seen in both curves, suggesting that performance has improved over time. Training that is balanced and successful is indicated by both curves increasing steadily and without divergence.

The proposed model achieved 82.33% accuracy, 76.70% precision, 89.15% recall, and an 82.46% F1-score. These evaluation results show that the model performs well in distinguishing between real and fake videos. The training and validation curves represent stable learning behavior without signs of overfitting, while the confusion matrix confirms the high number of correct predictions. We propose the use of this system for real-world deployments in media forensics and content moderation platforms. The outcomes and implementation specifics of the deepfake detection system have been shown, and we started by explaining the experiment configuration, namely the instruments, libraries, and hardware used for performing the DL model training and testing process.

Table 5 presents a comparative performance analysis of the existing and proposed ResNet-50 models using four standard evaluation metrics: accuracy, precision, recall, and F1-score. The results demonstrate that the proposed model consistently outperforms the baseline ResNet-50 across all major performance indicators. Specifically, the existing ResNet-50 achieved an accuracy of 75.00%, whereas the proposed approach improved the classification accuracy to 82.33%, representing a significant enhancement in the model’s overall predictive capability.

Furthermore, the proposed model achieved a precision of 76.70%, a recall of 89.15%, and an F1-score of 82.46%, compared with the existing ResNet-50, which obtained 80.00%, 68.00%, and 77.00%, respectively. Although the proposed model exhibits a slightly lower precision than the baseline, it demonstrates a substantial improvement in recall, indicating a much stronger ability to correctly identify positive instances while significantly reducing false negatives. The higher F1-score confirms that the proposed approach provides a better balance between precision and recall, leading to superior overall classification performance. These results validate the effectiveness of the proposed enhancements to the ResNet-50 architecture and demonstrate its suitability for accurate and reliable classification in the target application.

The surroundings were properly organized to be acceptable, so that the system could be efficiently trained and tested precisely. The evaluation metrics were accuracy, precision, recall, and F1-score. They assisted in assessing the effectiveness of the model in identifying deepfake videos versus real videos. A confusion matrix was also used to provide a clear insight into the correct or wrong number of videos that were identified as real or fake. It assisted in determining the strengths and weaknesses of the model, in particular, where the model went wrong. The training and validation accuracy were also plotted, demonstrating the level of model performance during training and on validation data. These outcomes proved that the model was not overfitting.

This study introduces a deepfake video detection system based on DL, namely a CNN model based on ResNet50. The utilization of deepfakes represents a significant problem because it leads to false information, reputation destruction, a potential impact on national security. Our system includes preprocessing of videos by the extraction of certain important frames, identifying and scaling faces, and converting them to a suitable format. The ResNet50 model used has a particularly strong architecture in terms of its image recognition abilities, and the model’s performance, indicated by the accuracy, precision, recall, and F1-score, was reasonably good. Although there are limitations to this study, such as the large amounts of data and high-performance equipment required, this study shows that DL is potentially useful when combating fake media and misinformation.

However, within the scope of this work on detecting deepfake videos through the classification and visual analysis of images, there are many domains in which improvements can be achieved. Another potential direction of study is related to introducing audio analysis, because many deepfakes also exploit speech editing by using AI-generated voice models. Future research may focus on speech patterns, tone, frequency, and lip sync to analyze videos using DL and signal processing, aiming for higher accuracy in detecting elements such as inconsistencies between audio and audiovisual data. Another critical area is the improvement of the user interface.