This document summarizes a presentation on detecting digital image forgery using salient keypoints. It introduces common types of image forgery and clues that reveal forgery. A framework is proposed that selects salient keypoints using distinctiveness, detectability, and repeatability to reduce keypoints and detect copy-move forgery. The approach uses SIFT and KAZE features and achieves promising results on standard datasets, outperforming other methods with lower false positive rates and higher precision and F1 scores. Future work could detect other forgery types and develop more robust detection algorithms.

1. Research Scholar’s Conclave presentation
on
Digital Image Forgery Detection using
Salient Keypoint Selection
Presented By:
Nitish Kumar
Ph.D. Scholar
Dept. of Electronics and Communication
Under the Guidance of:
Dr. Toshanlal Meenpal

2. Introduction
• With the advancement of image editing techniques, authenticity and reliability of digital image
has become very challenging.
• Realistic visual effect can be achieved in such a way that forged images are visually
indistinguishable from real ones.
• Image forgery aims to deliver deceptive information through the image graphic content.
• Image forensics verify the authenticity, ownership, and copyright of an image and detect changes
to the original image.
Fig.1 Example of a forged image

3. Types of Image Forgery:
Image Forgery
Copy-move
Forgery
Image Splicing
Forgery
Image
Inpainting
Forgery
Fig.2 Types of image forgery

4. Clues of Image Forgery:
Table I. Forgery detection clues for different forgery techniques
Tampering
Clues
Copy-move Inpainting Splicing
Region Duplication Yes Yes No
Edge Anomaly
(Sharp Edges)
Yes No Yes
Edge Anomaly
(Blurred Edges)
Yes No Yes
Region Anomaly
(JPEG Double
Quantization)
No Yes Yes
Region Anomaly
(Lighting
inconsistency)
No No Yes
Region Anomaly
(Camera trace
inconsistency)
No No Yes

5. Proposed Copy-move Forgery Detection
Fig.3 Framework of proposed salient keypoint-based copy-move image forgery detection

6. Selection of Salient Keypoints
• Salient keypoints are selected by
ranking the keypoints based on 3
parameters.
1) Distinctiveness: How different
the keypoint is from the rest of
the keypoints in the image.
2) Detectability: How robustly the
keypoints can be detected under
viewpoint/lighting changes.
3) Repeatability: It refers to the
ability of keypoints to remain
invariant to various
transformations.
Fig.4 Visualization of reduction in number of keypoints

7. Results and Discussion
Fig.5 Comparison of number of keypoints for four
different images of size 512 x 512.
Fig.6 Detection of copy-move forgery on CoMoFoD dataset
where a) original image, b) forged image c) detection result

8. Cont..
Author Methods FPR Precision F1 Score
Hashmi et. al DyWT and SIFT 10.00 88.89 85.00
Ojeniyi et al. DCT and SURF 6.36 93.86 95.45
Niyishaka et al. DoG and ORB - 90.09 86.24
Liu et. al CKN 7.27 93.16 96.03
Soni et al. SURF and 2NN 8.4 - -
Proposed Salient SIFT and KAZE 3.6 96.22 94.87
Table II. Comparison of proposed method with existing
techniques on the MICC-F220 dataset.
Author Methods FPR Precision F1 Score
Malviya et. al Auto colour correlogram 16 95.65 93.62
Soni et al. LBP-HP 7.40 - -
Mahmood et al. SWT - 95.76 96.05
Niyishaka et. al Blob and BRISK 9 96.84 94.35
El Biach et al. Encoder-decoder - - 81.56
Proposed Salient SIFT and KAZE 6 97.90 95.73
Table III. Comparison of proposed method with existing
techniques on the CoMoFoD dataset.

9. Conclusion and Future Scope
• In this work keypoint-based copy–move forgery detection has been proposed
using SIFT and KAZE features.
• Salient keypoints are selected for reduction in number of keypoints.
• Proposed detection approach has been evaluated on CoMoFoD and MICC-F220
datasets and gives promising results under geometric transformations and common
post-processing operations.
• As a future work, detection of other image forgery approach can be proposed.
• Robust detection algorithm can also be proposed which can detect any kind of
image forgery.

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