DeepNet: Protection of deepfake images with aid of deep learning networks

In the present information age, multimedia security has become a challenging task. Especially increased usage of images as multimedia data has been a key aspect in this digital transmission era. Deep fake detection of images is a real-time problem which needs to be focused. To resolve this challenge, a novel deep fake detection algorithm is proposed in this article. The presented research uses the Viola-Jones detection algorithm for efficient deep fake image detection. To protect the integrity of these images, the multiresolution domain approach is effectively utilized with redundant discrete wavelet transform (RDWT) and multiresolution singular value decomposition (MSVD). Discrete cosine transform (DCT) is applied for the extraction of frequency components. An adaptive neuro-fuzzy inference system (ANFIS)-based optimization is applied to attain the optimum weighing factor (WF). This WF exhibits a better trade-off among attributes of watermarking. Furthermore, authentication is successfully implemented with the aid of various deep learning models such as SqueezeNet, EfficientNet-B0, ResNet-50 and InceptionV3. This implementation explores the various aspects related to the ownership assertion. Analysis of comprehensive simulation results depicts the effectiveness of the proposed technique over different prevailing techniques. With the development of the proposed technique, deep fake image detection can easily be realized and safeguards the images. The average percentage improvement in the imperceptibility of the proposed technique is 52.14% and for robustness is 7.51%.