Q2. What are the future works mentioned in the paper "Robust hashing for image authentication using quaternion discrete fourier transform and log-polar transform" ?
By exploiting the QDFT into the log-polar domain, the authors better take into account the three image color planes and the resulting hash is robust to rotation attacks as well as to common image content-preserving operations. Their future work will focus on designing a robust hash capable to locate tampered regions of an image as well as to determine the type of the tampering.
Q3. What is the method for locating tampered regions?
Since the wavelet transform has a good time-frequency localization property, their method can locate tampered regions with a good accuracy but at the price of a longer image hash.
Q4. What is the method for obtaining the image features?
Discrete wavelet transform (DWT) -based image hashing methods: Ahmed et al. [1] used a wavelet transform to extract the image features.
Q5. What is the method for image hashing?
Matrix decomposition-based image hashing methods: Kozat et al. [17] used singular value decomposition (SVD) to get robust image features and to generate an image hash.
Q6. What other methods were reported for constructing image hashes?
Other robust feature extraction methods for constructing image hashes were also reported including the random Gabor filtering [22], the ring partition [23] and shape contexts [24].
Q7. What is the importance of image hashing?
Regarding image authentication, a robust image hash should also have good anti-collision (discriminative) capability for visually distinct images as well as a satisfactory level of security in order to make very difficult for an adversary to forge the hash value.
Q8. What is the method for generating an image hash?
Battiato et al. [21] adopted an image representation based on a set of SIFT features (called “bag of features”, BOF) to construct the hash and explored a non-uniform quantization of histograms of oriented gradients (HOG) to get tamper localization capabilities.
Q9. What are the three basic steps of image hashing?
In general, the construction of an image hash is based on three basic steps, i.e., pre-processing, image feature extraction and construction of hash.
Q10. What is the reason why the hash is able to be robust?
This perceptual robustness can be achieved for three main reasons: (i) the generated hash utilizes the low-frequency QDFT coefficients whose interrelation is hardly changed by content-preserving operations; (ii) the average preprocessing filter makes the method more robust against content-preserving operations such as noise, filtering operation and JPEG compression; (iii) the magnitude of QDFT coefficients of the log-polar transformed image is invariant to rotation.
Q11. What is the definition of a robust image hash?
image hashing methods extract essential image features from which a short binary or real number sequence, called hash, is generated to represent the image content.
Q12. Why is the QDFT3 robust to content preservation operations?
This is due to the joint combination of (i) the low-frequency magnitude coefficients of QDFT, (ii) the log-polar transform of the image central part, and (iii) the filtering operationenhances its robustness and increases its discriminative capability.
Q13. What are the operations that affect the averaging of the image?
These operations include pepper & salt noise, Gaussian noise, brightness adjustment, different filters, JPEG compression, cropping, scaling and rotation.
Q14. What are the results of the comparison of the QDFT3 methods?
Fig. 12(e), (g), (j) and (k) show that the QDFT3 is not robust to contrast adjustment, median filtering, rotation and cropping attack.
Q15. What is the method for capturing texture properties?
Liu et al. [15] utilized the wave atom transform to extract image features arguing that this approach has a sparse expansion and is capable to better capture texture properties.
Q16. How long does the DFT method take to generate a hash?
It can be seen that the time running for the five methods is very short and less than 0.22 s. The DFT method [19] is the fastest and needs only 0.07 s.Table 3 Overall performance comparison of hashing algorithms DFT [16] Zhao [3] NMF [19] QDFT[34]