Image Splicing Forgery Detection Techniques: A Review

Meena, Kunj Bihari; Tyagi, Vipin

doi:10.1007/978-3-030-88244-0_35

Cited by 11 publications

(7 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several recent investigations into image forgery detection have been reported [34], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49] of which a few reviewed deep learning (DL)-based techniques [50], [51], [52]. In this survey, we investigate recent trends in image forgery detection approaches for two of the most popular types of image forgery: copy-move images and spliced images.…”

Section: Review Of Recently Published Surveys On Ifd Problemsmentioning

confidence: 99%

“…All the IFD methods examined in this survey used handcrafted features to detect tampering. These types of tampering were also analyzed using DL-based methods [50]. Nonetheless, these researchers mainly discussed detection techniques in different categories without providing deep insights into DL architectures.…”

Section: Review Of Recently Published Surveys On Ifd Problemsmentioning

confidence: 99%

See 1 more Smart Citation

Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Pham

Park

2023

IEEE Access

View full text Add to dashboard Cite

In the last decades, deep learning (DL) has emerged as a powerful and dominant technique for solving challenging problems in various fields. Likewise, in the field of digital image forensics, a large and growing body of literature investigates DL-based techniques for detecting and classifying tampered regions in images. This article aims to provides a comprehensive survey of state-of-the-art DL-based methods for image-forgery detection. Copy-move images and spliced images, two of the most popular types of forged images, were considered. Recently, owing to advances in DL, DL-based approaches have yielded much better results as compared to traditional non-DL-based ones. The surveyed techniques were proposed by developing or fusing various efficient DL methods, such as CNN, RCNN, or LSTM to adapt to detecting tampered traces. INDEX TERMSCopy-move image, image forgery, spliced image.

show abstract

Section: Review Of Recently Published Surveys On Ifd Problemsmentioning

confidence: 99%

Section: Review Of Recently Published Surveys On Ifd Problemsmentioning

confidence: 99%

Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Pham

Park

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The source images used to generate a spliced image may include dissimilar color temperatures, illumination conditions, and noise levels based on various factors. Average filtering or some other related image processing operation can be applied as postprocessing like resizing, cropping, rotating, and retouching each of the source images to match the visual attributes, shape, and size of the target image so that the forged image can look realistic [5].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Digital Image Forgery Detection Using Transfer Learning

Khalil,

Ghalwash,

Elsayed

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Nowadays, digital images are a main source of shared information in social media. Meanwhile, malicious software can forge such images for fake information. So, it's crucial to identify these forgeries. This problem was tackled in the literature by various digital image forgery detection techniques. But most of these techniques are tied to detecting only one type of forgery, such as image splicing or copy-move that is not applied in real life. This paper proposes an approach, to enhance digital image forgery detection using deep learning techniques via transfer learning to uncover two types of image forgery at the same time, The proposed technique relies on discovering the compressed quality of the forged area, which normally differs from the compressed quality of the rest of the image. A deep learning-based model is proposed to detect forgery in digital images, by calculating the difference between the original image and its compressed version, to produce a featured image as an input to the pre-trained model to train the model after removing its classifier and adding a new fine-tuned classifier. A comparison between eight different pre-trained models adapted for binary classification is done. The experimental results show that applying the technique using the adapted eight different pre-trained models outperforms the state-of-the-art methods after comparing it with the resulting evaluation metrics, charts, and graphs. Moreover, the results show that using the technique with the pre-trained model MobileNetV2 has the highest detection accuracy rate (around 95%) with fewer training parameters, leading to faster training time.

show abstract

“…Retouching refers to the image manipulation techniques in which some features or small details within the target image are enhanced or concealed. In general, not only retouching can be used to conceal some information and convey falsified information, but it is also a commonly used technique to create images for advertisement and commercial purposes [1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…Splicing [1], on the other hand, refers to the combining of parts of images from two (or more) different sources to create a forge image. Utilizing splicing technique, the attacker can create a counterfeit information or evidence to trick people, create misunderstanding or to avoid being suspected during criminal investigation.…”

Section: Introductionmentioning

confidence: 99%

Study and Analysis of Copy-move Forgery Detection with Local Binary Pattern Method

RAO

Teerakanok

Uehara

2022

Preprint

View full text Add to dashboard Cite

With the advancement of technology, new problems and challenges also follow, among which the more serious problem is media forgery. Forged media information brings a lot of inconvenience to life. It is difficult to distinguish the truth from the false. In this article, various types of forgery are listed and elaborated with a focus on the copy-move forgery category, the study and research involving copy-move forgery(CMF) detection techniques using the Local Binary Pattern (LBP) are presented. The technical review of recent state-of-the-art LBP-based is provided.

show abstract

Image Splicing Forgery Detection Techniques: A Review

Cited by 11 publications

References 96 publications

Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

Enhancing Digital Image Forgery Detection Using Transfer Learning

Study and Analysis of Copy-move Forgery Detection with Local Binary Pattern Method

Contact Info

Product

Resources

About