05-02-2016, 02:35 PM
I need source code for tamper detection in images using image hash
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image tampering detection source code in matlab
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I need source code for tamper detection in images using image hash
06-02-2016, 12:06 PM
image tampering detection source code in matlab
Image Forensics
An image forgery.
The trustworthiness of photographs has an essential role in many areas, including: forensic investigation, criminal investigation, surveillance systems, intelligence services, medical imaging, and journalism. The art of making image fakery has a long history. But, in today’s digital age, it is possible to very easily change the information represented by an image without leaving any obvious traces of tampering. Despite this, no system yet exists which accomplishes effectively and accurately the image tampering detection task.
The digital information revolution and issues concerned with multimedia security have also generated several approaches to digital forensics and tampering detection. Generally, these approaches could be divided into active and passive–blind approaches. The area of active methods simply can be divided into the data hiding approach (e.g., watermarks) and the digital signature approach. We focus on blind methods, as they are regarded as a new direction and in contrast to active methods, they work in absence of any protecting techniques and without using any prior information about the image. To detect the traces of tampering, blind methods use the image function and the fact that forgeries can bring into the image specific detectable changes (e.g., statistical changes).
When digital watermarks or signatures are not available, the blind approach is the only way how to make the decision about the trustworthiness of the investigated image. Image forensics is a burgeoning research field and promise a significant improvement in forgery detection in the never–ending competition between image forgery creators and image forgery detectors.
Topics
Detecting traces of resampling
When two or more images are spliced together , to create high quality and consistent image forgeries, almost always geometric transformations such as scaling, rotation or skewing are needed.
Geometric transformations typically require a resampling and interpolation step. Thus, having available sophisticated resampling/interpolation detectors is very valuable.
Detecting near–duplicated image regions
In a common type of digital image forgery, called copy–move forgery, a part of the image is copied and pasted into the another part of the same image, typically with the intention to hide an object or a region . The copy–move forgery brings into the image several near–duplicated image regions.
Noise inconsistencies analysis
A commonly used tool to conceal traces of tampering is addition of locally random noise to the altered image regions. This operation may cause inconsistencies in the images noise . Therefore, the detection of various noise levels in an image may signify tampering.
Application of cyclostationarity analysis to image forensics
The term cyclostationarity refers to a special class of signals (cyslostationary signals) which exhibit periodicity in their statistics. Employing the existing efficient cyclostationary detectors to find the traces of geometric transformation shows promising results.
Other analyzed topics: double JPEG compression, CFA analysis, quantization tables analysis, etc.
Image Forensics
An image forgery.
The trustworthiness of photographs has an essential role in many areas, including: forensic investigation, criminal investigation, surveillance systems, intelligence services, medical imaging, and journalism. The art of making image fakery has a long history. But, in today’s digital age, it is possible to very easily change the information represented by an image without leaving any obvious traces of tampering. Despite this, no system yet exists which accomplishes effectively and accurately the image tampering detection task.
The digital information revolution and issues concerned with multimedia security have also generated several approaches to digital forensics and tampering detection. Generally, these approaches could be divided into active and passive–blind approaches. The area of active methods simply can be divided into the data hiding approach (e.g., watermarks) and the digital signature approach. We focus on blind methods, as they are regarded as a new direction and in contrast to active methods, they work in absence of any protecting techniques and without using any prior information about the image. To detect the traces of tampering, blind methods use the image function and the fact that forgeries can bring into the image specific detectable changes (e.g., statistical changes).
When digital watermarks or signatures are not available, the blind approach is the only way how to make the decision about the trustworthiness of the investigated image. Image forensics is a burgeoning research field and promise a significant improvement in forgery detection in the never–ending competition between image forgery creators and image forgery detectors.
Topics
Detecting traces of resampling
When two or more images are spliced together , to create high quality and consistent image forgeries, almost always geometric transformations such as scaling, rotation or skewing are needed.
Geometric transformations typically require a resampling and interpolation step. Thus, having available sophisticated resampling/interpolation detectors is very valuable.
Detecting near–duplicated image regions
In a common type of digital image forgery, called copy–move forgery, a part of the image is copied and pasted into the another part of the same image, typically with the intention to hide an object or a region . The copy–move forgery brings into the image several near–duplicated image regions.
Noise inconsistencies analysis
A commonly used tool to conceal traces of tampering is addition of locally random noise to the altered image regions. This operation may cause inconsistencies in the images noise . Therefore, the detection of various noise levels in an image may signify tampering.
Application of cyclostationarity analysis to image forensics
The term cyclostationarity refers to a special class of signals (cyslostationary signals) which exhibit periodicity in their statistics. Employing the existing efficient cyclostationary detectors to find the traces of geometric transformation shows promising results.
Other analyzed topics: double JPEG compression, CFA analysis, quantization tables analysis, etc.
05-10-2016, 03:26 PM
Please, I need more information about this topic especially the source code. My email address is oyalaw[at]yahoo.com
Thank you
Lawrence
Thank you
Lawrence
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