Camera Identification Algorithm Based on Sensor Pattern Noise Using Wavelet Transform, SVD / PCA and SVM Classifier
محورهای موضوعی : Machine learning
Kimia Bolouri
1
(Payam Noor)
Mehdi Javanmard
2
(Payam Noor)
Mohammad Firouzmand
3
(Iranian Research Organization for Science and Technology (IROST))
کلید واژه: Sensor Pattern Noise, Camera Sensor Pattern Noise, Source Camera Identification, Photo Response Non-Uniformity, Wavelet Transform,
چکیده مقاله :
Identifying the source camera of an image is one of the most important issues of digital court and is useful in many applications, such as images that are presented in court as evidence. In many methods, the image noise characteristics, extraction of Sensor Pattern Noise and its correlation with non-uniformity of the light response (PNU) are used. In this paper we have presented a method based on photo response non uniformity (PRNU) that provides some features for classification by support vector machine (SVM). Because the noise model is affected by the complexity of the image, we used the wavelet transform to de-noise and reduce edge effects in PRNU noise pattern and also raise the detection accuracy. We also used the Precision processing theory to reduce the image size, then we simplified and summarized the data using the Single Value Decomposition (SVD) Or principal component analysis (PCA). The results show that using two-level wavelet transform and summarized data is more suitable using PCA.
Identifying the source camera of an image is one of the most important issues of digital court and is useful in many applications, such as images that are presented in court as evidence. In many methods, the image noise characteristics, extraction of Sensor Pattern Noise and its correlation with non-uniformity of the light response (PNU) are used. In this paper we have presented a method based on photo response non uniformity (PRNU) that provides some features for classification by support vector machine (SVM). Because the noise model is affected by the complexity of the image, we used the wavelet transform to de-noise and reduce edge effects in PRNU noise pattern and also raise the detection accuracy. We also used the Precision processing theory to reduce the image size, then we simplified and summarized the data using the Single Value Decomposition (SVD) Or principal component analysis (PCA). The results show that using two-level wavelet transform and summarized data is more suitable using PCA.
