This study proposes a novel deepfake detection method that integrates the Integrate-backward-integrate (IbI) Logic Optimization Algorithm with Convolutional Neural Networks (CNNs) to enhance the detection of manipulated media content. The approach involves a multi-phase iterative process where the CNN is first trained on a diverse dataset of real and deepfake images. The trained CNN is then used for forward integration to classify images as real or deepfake. The IbI algorithm then performs a backward optimization phase, using feedback from the CNN's performance to iteratively refine the network's parameters, architecture, and feature extraction capabilities. This iterative optimization aims to improve the CNN's ability to discern subtle nuances between authentic and manipulated visuals. The re-integration phase evaluates the refined CNN's performance through multiple iterations, seeking to iteratively improve deepfake detection accuracy. Validation is conducted using separate datasets to prevent overfitting and ensure the model's generalizability. The proposed method aims to enhance the CNN's adaptability to evolving deepfake techniques, addressing the dynamic nature of manipulative media creation. The fusion of IbI Logic Optimization with CNNs presents a promising avenue for bolstering deepfake detection capabilities. However, the effectiveness of this approach relies on dataset quality, network architecture, and the dynamic nature of deepfake generation techniques. Continuous refinement and validation are essential to adapt the model to new challenges posed by advancing deepfake technologies. The methodology involves preprocessing steps such as normalization, resizing, cropping, color space conversion, histogram equalization, and noise reduction to prepare input images for deepfake detection. The CNN is trained using the Adam optimizer with a learning rate of 0.001 for 50 epochs with a batch size of 32. Data augmentation techniques such as random horizontal flips and rotations are applied to improve generalization. The model's performance is evaluated using accuracy, precision, recall, F1-score, ROC curve, and AUC. The proposed method outperforms existing methodologies in terms of accuracy, precision, recall, F1-score, and AUC, demonstrating its effectiveness in detecting deepfake content. The results show that the proposed method achieves higher performance metrics compared to existing deepfake detection methodologies, indicating its potential for practical applications in combating deepfake technology.This study proposes a novel deepfake detection method that integrates the Integrate-backward-integrate (IbI) Logic Optimization Algorithm with Convolutional Neural Networks (CNNs) to enhance the detection of manipulated media content. The approach involves a multi-phase iterative process where the CNN is first trained on a diverse dataset of real and deepfake images. The trained CNN is then used for forward integration to classify images as real or deepfake. The IbI algorithm then performs a backward optimization phase, using feedback from the CNN's performance to iteratively refine the network's parameters, architecture, and feature extraction capabilities. This iterative optimization aims to improve the CNN's ability to discern subtle nuances between authentic and manipulated visuals. The re-integration phase evaluates the refined CNN's performance through multiple iterations, seeking to iteratively improve deepfake detection accuracy. Validation is conducted using separate datasets to prevent overfitting and ensure the model's generalizability. The proposed method aims to enhance the CNN's adaptability to evolving deepfake techniques, addressing the dynamic nature of manipulative media creation. The fusion of IbI Logic Optimization with CNNs presents a promising avenue for bolstering deepfake detection capabilities. However, the effectiveness of this approach relies on dataset quality, network architecture, and the dynamic nature of deepfake generation techniques. Continuous refinement and validation are essential to adapt the model to new challenges posed by advancing deepfake technologies. The methodology involves preprocessing steps such as normalization, resizing, cropping, color space conversion, histogram equalization, and noise reduction to prepare input images for deepfake detection. The CNN is trained using the Adam optimizer with a learning rate of 0.001 for 50 epochs with a batch size of 32. Data augmentation techniques such as random horizontal flips and rotations are applied to improve generalization. The model's performance is evaluated using accuracy, precision, recall, F1-score, ROC curve, and AUC. The proposed method outperforms existing methodologies in terms of accuracy, precision, recall, F1-score, and AUC, demonstrating its effectiveness in detecting deepfake content. The results show that the proposed method achieves higher performance metrics compared to existing deepfake detection methodologies, indicating its potential for practical applications in combating deepfake technology.