Novel framework for optimized digital forensic for mitigating complex image attacks

Abstract

Digital Image Forensic is significantly becoming popular owing to the increasing usage of the images as a media of information propagation. However, owing to the presence of various image editing tools and softwares, there is also an increasing threats over image content security. Reviewing the existing approaches of identify the traces or artifacts states that there is a large scope of optimization to be implmentation to further enhance teh processing. Therfore, this paper presents a novel framework that performs cost effective optmization of digital forensic tehnqiue with an idea of accurately localizing teh area of tampering as well as offers a capability to mitigate the attacks of various form. The study outcome shows that propsoed system offers better outcome in contrast to existing system to a significant scale to prove that minor novelty in design attribute could induce better improvement with respect to accuracy as well as resilience toward all potential image threats.

Efficient resampling features and convolution neural network model for image forgery detection

“…Using (6) we can state that 𝜇 𝑗,𝑘 = 1 provided 𝑗 = 𝑘; otherwise 𝜇 𝑗,𝑘 = 0. The parameters 𝑗 ↓ (𝑑) and 𝑗 ↑ (𝑑) define the feature vector with the smallest and largest 𝑑 𝑡ℎ component.…”

“…The emergence of social networks in our daily life spurs the advent of various digital image editing tools which leads to belief issues of multimedia content being circulated. Designing high-quality tampering employing a machine learning model that is visually undetectable through the human eye [1], [2] is well within reach of the user through the following tool such as FaceApp [3], Adobe Sensiei [4], DeepPhoto editor [5], and Adobe sky Replace [6]. Thus, classifying an image as tampered with or not is becoming an extremely difficult task.…”

Extraction of image resampling using correlation aware convolution neural networks for image tampering detection

“…In recent times, the number of deep learning-based tampering detection [10]- [12] such as convolutional neural networks (CNN) [13]- [15], long-short term memory (LSTM) and stacked auto-encoders (SAE) [16] have been presented. In media crime scene investigation, the majority of state-of-art tampering detection methodologies have focused on detecting certain types of tampering only such as splicing [17] and copy-clone [18], [19]. As a result, these methodologies cannot be used for detecting hybrid tampering detection.…”