Digital image forgery detection is crucial in addressing the rapid spread of fake information through manipulated images, especially on social media platforms. Traditional techniques often focus on specific types of forgery, limiting their effectiveness in real-world scenarios. Traditional methods heavily depend on manual feature engineering, which often results in overlooked manipulations, decreased accuracy, adaptability, and scalability issues when handling large datasets or high-resolution images. Deep learning has emerged as a powerful tool for addressing the challenges associated with image forgery detection. The proposed work introduces an innovative method for detecting image forgeries using deep learning techniques, employing convolutional neural networks (CNNs) and specifically evaluating the performance of the EfficientNetb7 model. This method leverages transfer learning to detect copy-move image forgery. It involves generating featured images by calculating the difference between the input image and compressed versions, which are then fed into pre-trained CNN model. The model undergo fine-tuning to adapt to forgery detection. Additionally, the output of the forgery detection process includes both text and audio. This combination enhances the accessibility and interpretability of the detection results, making them more understandable for users with different sensory preferences or impairments. This added feature ensures that the detection outcomes are easily comprehensible and usable across a broader range of users and applications.
Efficient routing techniques are critical in a wireless sensor network (WSN) to extend network lifetime and preserve energy, providing uninterrupted data transfer across a wide range of applications. In this research, we intend to develop a novel dynamic waterwheel plant optimization (DWPO) strategy for efficient path planning with blockchain (BC) for enhancing cybersecurity in WSN. Our proposed approach utilizes a fitness function that incorporates inter cluster distance along with other relevant parameters. This function strategically contributes to the selection of ideal routes, contributing to the efficacy of our method for improving cybersecurity in WSN contexts. The experimental validation of the DWPO model is conducted using the MATLAB program and examined in terms of the network lifetime ($93.6 \%$), packet delivery ratio ($90.2 \%$), and energy consumption ($\mathbf{1 8. 2} \mathrm{J}$). The experimental results illustrate that the proposed DWPO approach performed better than the other existing approaches for protecting the routing nodes through efficient path planning with BC-assisted DWPO in a WSN.
There are significant cybersecurity challenges that face wireless sensor networks (WSNs) as a result of their decentralized nature and limited resources although they are highly important in most fields. Traditional security mechanisms frequently fail to cope with the changing and diverse conditions in WSNs. To reduce data transfer but maintain WSNs sensor saturation and data security, this work proposes a prediction-based data fusion and sensing strategy. The suggested method called the ARIMA-SK-EELM system, which is made up of autoregressive integrated moving average (ARIMA), stable kernel-enhanced extreme learning machine (SK-EELM), and Threefish algorithm (TFA). In the procedure on data sensing and fusion, ARIMA predicts initially from a few data elements, SK-EELM for precise accuracy on initial expected value similar to actual value while TFA is used during transmissions for both encoded and decoded data. This paper introduces an ARIMA-SK-EELM model with high predictability, low interferences, strong scalability, and secrecy. The results of simulation show that this technique suggested can be effective in reducing unnecessary transfers by accurate forecasting.
