Image source acquisition identification of mobile devices based on the use of features

Orozco, Ana Lucila Sandoval; Corripio, Jocelin Rosales; Villalba, Luis Javier García; Castro, Julio César Hernández

doi:10.1007/s11042-015-2633-2

Cited by 9 publications

(8 citation statements)

References 36 publications

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“…As is known, SPN has been used to identify source cameras of NIs and has obtained excellent performance [10,11,16]. However, there is no SPN in CGs.…”

Section: The Proposed Methodsmentioning

confidence: 99%

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Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Yao

Zhang

et al. 2018

Sensors

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Computer-generated graphics (CGs) are images generated by computer software. The rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images—CGs and NIs—are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.

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“…As is known, SPN has been used to identify source cameras of NIs and has obtained excellent performance [10,11,16]. However, there is no SPN in CGs.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…This has been named “sensor pattern noise” (SPN) and is used as a fingerprint to characterize an individual camera. In particular, SPN has been used in image forgery detection [15] and source camera identification [16]. …”

Section: Related Workmentioning

confidence: 99%

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Yao

Zhang

et al. 2018

Sensors

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“…The researchers also put forward some effective features to distinguish and identify the digital mobile and CG images, such as CFA interpolation periodicity [5], visual characteristics [6], texture feature [7,8], white balance [9], and local binary pattern(LBP) [10]. For more different types of mobile devices, Orozco used color features and quality features [11]. 2Mobile digital image sources are taken as the evidence by mobile device model: the same tomography hardware and the same image processing algorithm is used in mobiles with the same device model, so we use the characteristics of hardware device and image processing algorithm to be the important foundation for mobile image forensics.…”

Section: Introductionmentioning

confidence: 99%

Secure and efficient DRM watermark algorithm of forensics in mobile internet

Jiang

2018

J Image Video Proc.

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With the development of mobile Internet technology, the characteristic of easy editing, transmitting, and forging the digital media bring great challenges in authenticity of multimedia. Due to that, the focus on the digital forensics and identification, such as source identification, content authentication, and information integrity, have become an important research content in the field network security, forensic science and other fields. To solve this problem, in this paper, we proposed an image digital rights management scheme for mobile Internet forensics based on watermark with high-level security and traceability. In this scheme, we embed some user-identity-related and device-related information of mobile phones as a source identification of snapped photos and stored pictures; once the forwarded image data from mobile phones are misused such as spreading the image data on Internet without authorization, especially the confidential image data, we can trace the misuse responsibility by extracting the mobile phone data embedded in the mobile photos. Finally, we evaluate the proposed security and efficiency of the DRM watermark scheme for mobile Internet, a large amount of experiments manifest the proposed watermark scheme is robust, secure, and efficient for the protection and misuse tracing of mobile image data.

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“…Many techniques have been proposed for camera identification in the last two decades [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. An identifier is extracted and associated with a camera as a fingerprint that makes it distinguishable from another one (of the same brand or even of the same model).…”

Section: Introductionmentioning

confidence: 99%

Trusted Cameras on Mobile Devices Based on SRAM Physically Unclonable Functions

Arjona

Prada-Delgado

Arcenegui

et al. 2018

Sensors

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Nowadays, there is an increasing number of cameras placed on mobile devices connected to the Internet. Since these cameras acquire and process sensitive and vulnerable data in applications such as surveillance or monitoring, security is essential to avoid cyberattacks. However, cameras on mobile devices have constraints in size, computation and power consumption, so that lightweight security techniques should be considered. Camera identification techniques guarantee the origin of the data. Among the camera identification techniques, Physically Unclonable Functions (PUFs) allow generating unique, distinctive and unpredictable identifiers from the hardware of a device. PUFs are also very suitable to obfuscate secret keys (by binding them to the hardware of the device) and generate random sequences (employed as nonces). In this work, we propose a trusted camera based on PUFs and standard cryptographic algorithms. In addition, a protocol is proposed to protect the communication with the trusted camera, which satisfies authentication, confidentiality, integrity and freshness in the data communication. This is particularly interesting to carry out camera control actions and firmware updates. PUFs from Static Random Access Memories (SRAMs) are selected because cameras typically include SRAMs in its hardware. Therefore, additional hardware is not required and security techniques can be implemented at low cost. Experimental results are shown to prove how the proposed solution can be implemented with the SRAM of commercial Bluetooth Low Energy (BLE) chips included in the communication module of the camera. A proof of concept shows that the proposed solution can be implemented in low-cost cameras.

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Image source acquisition identification of mobile devices based on the use of features

Cited by 9 publications

References 36 publications

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Secure and efficient DRM watermark algorithm of forensics in mobile internet

Trusted Cameras on Mobile Devices Based on SRAM Physically Unclonable Functions

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