Digital Face Manipulation in Biometric Systems

Ibsen, Mathias; Rathgeb, Christian; Fischer, Daniel; Drozdowski, Pawel; Busch, Christoph

doi:10.1007/978-3-030-87664-7_2

Cited by 10 publications

(3 citation statements)

References 42 publications

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“…F ACIAL recognition technology has been successfully applied in numerous real-world applications, such as mobile payments, automated teller machines (ATMs), automatic border control, and surveillance. However, there are various physical and digital attacks, such as face manipulation attacks (e.g., deepfake, face swap) [1], face morphing [2], face adversarial attacks [3], and face spoofing (i.e., presentation attacks) [4], that can be utilized to spoof the biometric systems. Thus, designing reliable approaches for presentation attack detection (PAD) is vital to enhance the security of face recognition systems.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Spatio-temporal Global Sampling for Presentation Attack Detection

Muhammad

Zhang

Liu

et al. 2024

IEEE Trans. Circuits Syst. II

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Without developing dedicated countermeasures, facial biometric systems can be spoofed with printed photos, replay attacks, silicone masks, or even a 3D mask of a targeted person. Thus, the threat of presentation attacks needs to be addressed to strengthen the security of the biometric systems. Since a 2D convolutional neural network (CNN) captures static features from video frames, the camera motion might hinders the performance of modern CNNs for video-based presentation attack detection (PAD). Inspired by the egomotion theory, we introduce an adaptive spatiotemporal global sampling (ASGS) technique to compensate the camera motion and use the resulting estimation to encode the appearance and dynamics of the video sequences into a single RGB image. This is achieved by adaptively splitting the video into small segments and capturing their global motion within each segment. The proposed global motion is estimated based on four key steps: dense sampling, FREAK feature extraction and matching, similarity transformation, and aggregation function. This allows using deep models pre-trained on images for video-based PAD detection. Moreover, the interpretation of ASGS reveals that the most important parts for supporting the decision on PAD are consistent with motion cues associated with the artifacts, i.e., hand movement, material reflection, and expression changes. Extensive experiments on four standard face PAD databases demonstrate its effectiveness and encourage further study in this domain.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Spatio-temporal Global Sampling for Presentation Attack Detection

Muhammad

Zhang

Liu

et al. 2024

IEEE Trans. Circuits Syst. II

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“…They described the work in this field as "a cat and mouse game"; whereas the detection techniques are improved, the manipulation methods are improved. Many researches have been introduced tackling the manipulation detection issue; however, to the current date, there is no global reliable face manipulation detection technique, which means this field is still nascent [48][49][50]. One of the limitations that have been mentioned in [48] is the low detection accuracy in the GAN-based methods [51][52][53][54] when bad-quality input images are tested such as images in bad lighting conditions, noisy, blurry, and other low-resolution images.…”

Section: Introductionmentioning

confidence: 99%

A novel blind tamper detection and localization scheme for multiple faces in digital images

Thabit

2023

IET Image Processing

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Face image manipulation detection (FIMD) is a research area of great interest, widely applicable in fields requiring data security and authentication. Existing FIMD schemes aim to identify manipulations in digital face images, but they possess individual strengths and limitations. Most schemes can only detect specific manipulations under certain conditions, leading to variable success rates across different images. The literature lacks emphasis on detecting manipulations involving multiple faces. This paper introduces a novel blind tamper detection and localization scheme specifically designed for multiple faces in digital images. The proposed multiple faces manipulation detection (MFMD) scheme consists of two stages: face detection and selection, and image watermarking. Through extensive experiments, the MFMD scheme's performance has been evaluated on various multiple‐face images, considering embedding capacity, payload, watermarked image quality, time complexity, and manipulation detection ability. The results demonstrate the MFMD scheme's efficacy in detecting different types of manipulations for multiple faces in images. Furthermore, the watermarked images exhibit high visual quality, even when multiple faces are present. The scheme's efficiency recommends it for practical applications, especially in sharing personal images over unsecured networks. This research advances FIMD techniques by addressing the neglected area of multiple‐face manipulation detection. With improved accuracy, faster processing times, and resilience against various manipulations, the MFMD scheme offers valuable capabilities for enhancing data security and authentication in real‐world scenarios.

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“…The shared face images can increase the speed of various processes and documentation, facilitate working with different systems, overcome distance restrictions, share memories and personal information, access online applications, and many others. The shared face images can easily be manipulated using digital image processing applications, and the manipulations can be harmful or harmless based on the intentions of the manipulator [1]. The recent interest of the research community has been directed towards a type of manipulation called DeepFakes which utilizes deep-learning or machinelearning-based algorithms to create fake face images or videos that are hard to be recognized as fake information [2]- [6].…”

Section: Introductionmentioning

confidence: 99%

A New Face Region Recovery Algorithm based on Bicubic Interpolation

Al-Hadaad,

Thabit,

Zidan

2023

JOIV : Int. J. Inform. Visualization

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Recently, researchers focused on face image manipulation detection and localization techniques because of their importance in image security applications. The previous research has not highlighted the recovery of the face region after manipulation detection. This paper presents a new face region recovery algorithm (FRRA) to be included in the face image manipulation detection algorithms (FIMD). The proposed FRRA consists of two main algorithms: face data generation algorithm and face region restoration algorithm. Both algorithms start by detecting the face region using Multi-task Cascaded Neural Network followed by a face window selection process. In the face data generation algorithm, the recovery information is generated from the shirked face window using bicubic interpolation technique. In the face region restoration algorithm, the face region zoomed using bicubic interpolation technique. The proposed FRRA has been tested and compared with previous recovery methods for different color face images, and the results proved that the FRRA could recover the face region with better visual quality at the same data length compared to previous methods. The main contributions of this research are a) the suggestion of including a face region recovery algorithm to FIMD, b) the study of previous recovery data generation algorithms for color face images, and c) introducing a new algorithm for generating the recovery data based on bicubic interpolation. In the future, the proposed algorithm can be included in the recent FIMD algorithms to recover the face region, which can be very useful in practical applications, especially those used in data forensics systems.

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Digital Face Manipulation in Biometric Systems

Cited by 10 publications

References 42 publications

An Adaptive Spatio-temporal Global Sampling for Presentation Attack Detection

An Adaptive Spatio-temporal Global Sampling for Presentation Attack Detection

A novel blind tamper detection and localization scheme for multiple faces in digital images

A New Face Region Recovery Algorithm based on Bicubic Interpolation

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