## 1 Introduction  
The illegal filming of videos is increasing [1, 2, 3]. This means that \"digital sex crimes\" are rampant regardless of the popularity or occupation of the victim [4, 5]. According to the 2018 statistics of the Korean National Police Agency, nearly 16.2 cases of illegal filming occur each day, and the number of people arrested for illegal filming and distribution has been increasing annually. Most illegal filming cases involve the use of smartphones [6]. Illegally filmed images are distributed over social media, which support easy uploading and sharing. This indicates that digital sex crimes are more socially common than people realize. It also means that uploading and sharing illegally filmed images in SNS (Social Network Service) chat rooms without computation is rampant within society. When images filmed illegally through smartphones are distributed over social media, victims experience a violation of their human rights and find it tough to live a normal life. To minimize these consequences, it is necessary to detect and remove distributed images as quickly as possible. Many studies have been conducted on the investigation method to detect illegal distribution of videos. However, various anti-forensic techniques have also been evolving to keep up in the competition. When a criminal suspect maliciously edits an original video or creates clip videos, the file type and hash value of the video is changed, denying the universal forensic of using hash value. In particular, if a suspect distributes illegal videos on SNS, the size of the file is limited and inevitably edited. Eventually, we find that the method of analysis by comparing hash values in edited video files has limitations in accuracy. Despite this, not much research is done on resolving this issue. The only way available for the time being is to manually watch numerous videos one by one, which causes many problems. One of them is the lack of human resource and time to watch multiple videos. In addition, considering smartphones contain volumes of personal information, it is not easy to seize and investigate such devices due to privacy invasion concerns. Victims' secondary damage also needs to be taken into consideration. Therefore, a method for detecting original and manipulated images without opening multiple videos directly is required. This paper proposes an image extraction methodology for identifying all original and manipulated images through optimal forensics applied to the suspect's mobile device, without the need to open the videos individually. With the proposed method, the original and manipulated images are cropped in a frame unit, and the similarity between the original and manipulated images is analyzed using a histogram. The proposed method is capable of checking whether two images are the same and detecting images optimally to cope with increased crime. The contributions of the present study are as follows:  
* First, the proposed method shortens the investigation time. The distribution of original videos is detected using hash values. However, to detect manipulated images and videos, going through massive volumes of images one by one is unavoidable. Our proposed method is capable of optimally cropping a video into the unit of an image and detecting both original and manipulated videos more quickly than the manual methods. * Second, it makes it possible to avoid the limitations imposed by search and seizure laws. Because smartphones may include a lot of personal information, it is difficult to obtain search and seizure warrants for them. With the proposed method, we compare the similarity of histogram information among comparison images without checking the video files identified individually during device scanning. This paper comprises the following: Section 2 describes related studies on image similarity comparison algorithms and an optimal comparison between images. In Section 3, the overall process of this study and the proposed method are described.