Robust image hashing using progressive feature selection for tampering detection

Pun, Chi‐Man; Yan, Cai-Ping; Yuan, Xiaochen

doi:10.1007/s11042-017-4809-4

Cited by 23 publications

(9 citation statements)

References 26 publications

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“…Image authentication and tamper localization methods are also proposed. The results in [25] show robustness against different signal processing and geometric attacks. Fei et al [26] proposed a hash-based template matching algorithm for video sequences.…”

Section: Related Workmentioning

confidence: 84%

“…This is followed by hash construction from features that are extracted through QPCET moments. Pun et al [25] proposed a feature extraction method that combines local features of both structure and colour information. The hash is constructed through Horizontal Location-Context Hashing (HLCH) and Vertical Location-Context Hashing (VLCH) methods.…”

Section: Related Workmentioning

confidence: 99%

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A Smart and Robust Automatic Inspection of Printed Labels Using an Image Hashing Technique

Khan

Ahmed

Khan³

et al. 2022

Electronics

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This work is focused on the development of a smart and automatic inspection system for printed labels. This is a challenging problem to solve since the collected labels are typically subjected to a variety of geometric and non-geometric distortions. Even though these distortions do not affect the content of a label, they have a substantial impact on the pixel value of the label image. Second, the faulty area may be extremely small as compared to the overall size of the labelling system. A further necessity is the ability to locate and isolate faults. To overcome this issue, a robust image hashing approach for the detection of erroneous labels has been developed. Image hashing techniques are generally used in image authentication, social event detection and image copy detection. Most of the image hashing methods are computationally extensive and also misjudge the images processed through the geometric transformation. In this paper, we present a novel idea to detect the faults in labels by incorporating image hashing along with the traditional computer vision algorithms to reduce the processing time. It is possible to apply Speeded Up Robust Features (SURF) to acquire alignment parameters so that the scheme is resistant to geometric and other distortions. The statistical mean is employed to generate the hash value. Even though this feature is quite simple, it has been found to be extremely effective in terms of computing complexity and the precision with which faults are detected, as proven by the experimental findings. Experimental results show that the proposed technique achieved an accuracy of 90.12%.

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Section: Related Workmentioning

confidence: 84%

Section: Related Workmentioning

confidence: 99%

A Smart and Robust Automatic Inspection of Printed Labels Using an Image Hashing Technique

Khan

Ahmed

Khan³

et al. 2022

Electronics

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show abstract

“…Thus, this hypothesis was tested. An initial test was carried out on a set of 47 images from the same image pack used by the authors [ 37 ], and the variation of HD with the angle of rotation was plotted. As can be seen in Figure 7 , the results are similar to those presented in that work.…”

Section: Resultsmentioning

confidence: 99%

“…Qin et al [ 36 ] developed a method to attribute a final hash using color vector angle, and it was shown that it is secure even after quantizing and scrambling. Pun et al [ 37 ] designed a method for tampering detection using progressive feature point selection, which filters the key points as opposed to the Scale-Invariant Feature Transform (SIFT) method, and better detects the tamper feature. Dimension reduction-based methods are also compression methods, the difference being that they reduce the dimensional space into a lower one, based on the significance of each dimension.…”

Section: State Of the Artmentioning

confidence: 99%

Design and Comparison of Image Hashing Methods: A Case Study on Cork Stopper Unique Identification

et al. 2021

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Cork stoppers were shown to have unique characteristics that allow their use for authentication purposes in an anti-counterfeiting effort. This authentication process relies on the comparison between a user’s cork image and all registered cork images in the database of genuine items. With the growth of the database, this one-to-many comparison method becomes lengthier and therefore usefulness decreases. To tackle this problem, the present work designs and compares hashing-assisted image matching methods that can be used in cork stopper authentication. The analyzed approaches are the discrete cosine transform, wavelet transform, Radon transform, and other methods such as difference hash and average hash. The most successful approach uses a 1024-bit hash length and difference hash method providing a 98% accuracy rate. By transforming the image matching into a hash matching problem, the approach presented becomes almost 40 times faster when compared to the literature.

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“…Feature-Based Hashing. Later in 2018, several new techniques based on image features for the hashing algorithm were developed, which include extraction of structural features from color images [37], dual-cross pattern-based textural features [38], progressive feature point selection [39], and a geometric correction-based technique using local and global features to counter the rotation scaling translation (RST) attacks [40]. However, these algorithms are unable to verify the validity of all types of images from all across the globe.…”

Section: Statisticalmentioning

confidence: 99%

A Secure and Robust Autoencoder-Based Perceptual Image Hashing for Image Authentication

Shaik

Karsh

Islam

et al. 2022

Wireless Communications and Mobile Computing

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With the advancement of technology, it has become easier to modify or tamper with digital data effortlessly. In recent times, the image hashing algorithm has gained popularity for image authentication applications. In this paper, a convolutional stacked denoising autoencoder (CSDAE) is utilized for producing hash codes that are robust against different content preserving operations (CPOs). The CSDAE algorithm comprises mapping high-dimensional input data into hash codes while maintaining their semantic similarities. This implies that the images having similar content should have similar hash codes. To demonstrate the effectiveness of the model, the correlation between hash codes of semantically similar images has been evaluated. Subsequently, tampered localization is done by comparing the decoder output of the manipulated image with the hash of the actual image. Then, the localization ability of the model was measured by computing the f 1 scores between the predicted region and the original tampered region. Based on the comparative performance and receiver-operating characteristics (ROC) curve, we may conclude that the proposed hashing proposed algorithm provides improved performance compared to various state-of-the-art techniques.

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Robust image hashing using progressive feature selection for tampering detection

Cited by 23 publications

References 26 publications

A Smart and Robust Automatic Inspection of Printed Labels Using an Image Hashing Technique

A Smart and Robust Automatic Inspection of Printed Labels Using an Image Hashing Technique

Design and Comparison of Image Hashing Methods: A Case Study on Cork Stopper Unique Identification

A Secure and Robust Autoencoder-Based Perceptual Image Hashing for Image Authentication

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