CASIA Image Tampering Detection Evaluation Database

Dong, Jing; Wang, Wei; Tan, Tieniu

doi:10.1109/chinasip.2013.6625374

Cited by 527 publications

(295 citation statements)

References 9 publications

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“…For detecting color image splicing, we selected three datasets such as Columbia color DVMM [12], CASIA1, and CASIA2 [20]. The Columbia color image dataset consists of 183 authentic and 180 spliced images in TIFF format.…”

Section: Detection Results For Color Datasetsmentioning

confidence: 99%

“…Muhammad et al proposed an imposing image forgery detection method based on a steerable pyramid transform and local binary pattern with feature reduction [17]. This method demonstrated the best performance to rate of 97.33 % on CASIA2 dataset [20]. However, this scheme requires an enormous of features and additional feature selection techniques to reduce the number of features.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Image splicing detection based on inter-scale 2D joint characteristic function moments in wavelet domain

Park

Han

Moon

et al. 2016

J Image Video Proc.

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In this paper, we propose an image splicing detecting method using the characteristic function moments for the inter-scale co-occurrence matrix in the wavelet domain. We construct the co-occurrence matrices by using a pair of wavelet difference values across inter-scale wavelet subbands. In this process, we do not adopt the thresholding operation to prevent information loss. We extract the high-order characteristic function moments of the two-dimensional joint density function generated by the inter-scale co-concurrent matrices in order to detect image splicing forgery. Our method can be applied regardless of the color or gray image dataset using only luminance component of an image. By performing experimental simulations, we demonstrate that the proposed method achieves good performance in splicing detection. Our results show that the detection accuracy was greater than 95 % on average with well-known four splicing detection image datasets.

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Section: Detection Results For Color Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image splicing detection based on inter-scale 2D joint characteristic function moments in wavelet domain

Park

Han

Moon

et al. 2016

J Image Video Proc.

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show abstract

“…Since tampering generates a perceptually different image, therefore image hash of tampered images should be different from original ones. Further, we carried out the experiment to see the result of our algorithm towards malicious activities and verification for locating forged areas (discussed in Section 3.1) by using CASIA V2.0 tampered image database [42] with 800 selected image pairs. For example, some tampered image pairs and their experimental results are shown in Table 5.…”

Section: Analysis Of Sensitivity Towards Malicious Attacks (Forgery) mentioning

confidence: 99%

“…In the case of tampering (i.e., a small region is Â Ã from h ' where p i = [x i y i w i h i ], x i and y i are corner coordinates, w i and h i are width and height, of a circumscribed rectangle of ith salient region. It has been experimentally observed that 96% of 2800 images has the number of salient regions less than six, only a few of the images has larger than it (for example, shown in the second and third columns of Table 5 in the result Section 4. , where T ' = 2 selected empirically from the experiment on 800 tampered image pairs taken from CASIA V2.0 tampered image database [42]). The regions 3 and 4 (i.e., p 0 3 and p 0 4 respectively) may have been inserted by an adversary in the received image.…”

Section: Tamper Localizationmentioning

confidence: 99%

Robust image hashing through DWT-SVD and spectral residual method

Karsh

Laskar

Aditi

2017

J Image Video Proc.

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In the last few decades, the discovery of various methods for generating secure image hash has become revolutionary in the field of image hashing. This paper presents an efficient approach to obtain image hash through DWT-SVD and a saliency detection technique using spectral residual model. The latest image hashing technique based on ring partition and invariant vector distance is rotation invariant for the large angle at the cost of being insensitive to corner forgery. But, due to the use of the central orientation information, the proposed system is rotation invariant for arbitrary angles along with sensitiveness to corner changes. In addition, we have used the HSV color space that gives desirable classification performance. It provides satisfactory results against large degree rotation, JPEG compression, brightness and contrast adjustment, watermarking, etc. This technique is also sensitive to malicious activities. Moreover, it locates the forged areas of a forged image. We have applied the proposed algorithm to a large collection of images from various databases. The receiver operating characteristics shows that the proposed method is better than some state-of-the-art methods.

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“…Sensor tampering, which is also referred to as presentation attack [7] includes replay-attack and print-attack, which are all spoofing approaches to fool biometric sensors [6]. Database tampering includes splicing, copy/paste, inpainting and some pre-processing effects carried out maliciously on an authentic image [8]. Biometric systems are exposed to several attacks, but considering digital tampering, Ratha et al [9] noted that, templates stored in the database maybe modified or removed, or new templates may be introduced in the database.…”

mentioning

confidence: 99%

On the Use of Benford’s Law to Detect JPEG Biometric Data Tampering

Iorliam¹,

Caleb²

2017

JIS

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Tampering of biometric data has attracted a great deal of attention recently. Furthermore, there could be an intentional or accidental use of a particular biometric sample instead of another for a particular application. Therefore, there exists a need to propose a method to detect data tampering, as well as differentiate biometric samples in cases of intentional or accidental use for a different application. In this paper, fingerprint image tampering is studied. Furthermore, optically acquired fingerprints, synthetically generated fingerprints and contact-less acquired fingerprints are studied for separation purposes using the Benford's law divergence metric. Benford's law has shown in literature to be very effective in detecting tampering of natural images. In this paper, the Benford's law features with support vector machine are proposed for the detection of malicious tampering of JPEG fingerprint images. This method is aimed at protecting against insider attackers and hackers. This proposed method detected tampering effectively, with Equal Error Rate (EER) of 2.08%. Again, the experimental results illustrate that, optically acquired fingerprints, synthetically generated fingerprints and contact-less acquired fingerprints can be separated by the proposed method effectively.

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CASIA Image Tampering Detection Evaluation Database

Cited by 527 publications

References 9 publications

Image splicing detection based on inter-scale 2D joint characteristic function moments in wavelet domain

Image splicing detection based on inter-scale 2D joint characteristic function moments in wavelet domain

Robust image hashing through DWT-SVD and spectral residual method

On the Use of Benford’s Law to Detect JPEG Biometric Data Tampering

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