Enhancement of Image Forgery and Improvement of Image Parameters Using DWT Algorithm

Soni, Rajni; Amhia, Hemant

doi:10.1007/978-981-19-1559-8_1

Cited by 1 publication

(1 citation statement)

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“…The existing methods for comprehending and verifying digital evidence implicitly rely on the use of tools and expert representations to allay jurors' and judges' worries. Digital image fraud is now easily accomplished because to the ongoing advancements in computer technology and image processing application software [1,2]. The easiest way to sum up the state of forensics technologies now is to say that they are hard to use, hard to verify, and cumbersome to people's behavior.…”

Section: Introductionmentioning

confidence: 99%

PH-SIFT with PSO Algorithm: A Novel Approach to Detecting Forgery in High- Resolution Images

Mishra,

Jain,

Kumar

2024

Preprint

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The use of machine learning for forgery detection in image forgeries poses a significant danger to modern multimedia technologies. These forgery detectors appear promising; however they are known to be vulnerable to evasion attacks. A "forgery" is a malicious user established by an attacker that may change its appearance while spreading; this is known as polymorphism and metamorphosis. Common behavioral characteristics showing the source and intended use of multimedia devices are displayed by image forgeries. However, the majority of novel forgery detection variations frequently elude detection by traditional forgery detection methods. This paper proposes a new approach to improve the utility of an existing framework. We need to create a unique model, PH-SIFT using PSO Algorithm, in order to improve the overall forgery analysis because the current deep learning algorithm forgery detection and analysis. Applying the PH-SIFT utilizing PSO Algorithm technique, which also aims to improve its accuracy and other metrics, achieves this. This method can get around the complexity and challenges presented by different concerns related to forgery detection and analysis. Here, the datasets from forgery detection and analysis are used to train the proposed algorithm. Following training, the datasets are pre-processed by reducing training mistakes. The PSO Algorithm-built PH-SIFT then carries out the detection procedure to anticipate any forgery activity.

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Section: Introductionmentioning

confidence: 99%