Fast and Robust Identification Methods for JPEG Images with Various Compression Ratios

Abstract

We propose two identification methods for JPEG-coded images. The purposes are to identify the images that are compressed from the same original image with various compression ratios in fast and robust manner. The first approach can avoid identification leakage or false negative (FN), and could result in a few false positives (FP). The second approach can avoid both FN and FP, with a slightly longer processing time. By combining the two schemes, a faster and a more perfect identification can be achieved, in which FN and FP can be avoided.

Robust Image Identification without Visible Information for JPEG Images

“…3 [21], [22]. On the other hand, the positions of zero values do not provide any visible information, compared to the features used in [10], [11], [14]- [16]. Besides, the proposed scheme can identify images under various coding conditions, although schemes which Moreover, several image-hashing schemes have been proposed for image retrieval [18]- [20].…”

“…In the conventional schemes [10], [11], [14]- [16], the signs of coefficients are used as features. However, they have two important limitations.…”

“…This paper focuses on the former one, that has generally a strong robustness against a difference in compression parameters. Conventional schemes [10], [11], [13]- [16] not only are robust against a difference in compressed ratios but also do not produce false negative matches in various compression ratios, due to the use of the positive and negative signs of DCT coefficients. However, they need to be combined with a security technique with a secret key such as the fuzzy commitment scheme [14]- [16], to securely protect the features which have some visible information.…”

[Paper] Fuzzy Commitment Scheme-Based Secure Identification for JPEG XR Images under Various Compression Ratios

“…So far, several identification schemes and image hash functions have been developed for authenticating images [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. They can be broadly classified into two types according to a difference in extracted features: compression method-depended type, to which the proposed scheme corresponds, and compression methodindependent type.…”

“…Most of these schemes not only are robust against a difference in compressed levels but also do not produce false negative matches in any compression level. However, the conventional schemes [3][4][5][6][7][9][10][11] do not consider to identify images securely, since it is impossible to avoid the effect caused by a difference in compression levels.…”