Are You Lying: Validating the Time-Location of Outdoor Images

Li, Xiaopeng; Xu, Wenyuan; Wang, Song; Qu, Xianshan

doi:10.1007/978-3-319-61204-1_6

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…Simon et al [48] utilized video cameras and microphones to infer PINs entered on a number-only soft keyboard on a smartphone. Li et al [33] can verify the capture time and location of the photos with the sun position estimated based on the shadows in the photo and sensor readings of the cameras. Sun et al [55] presented a video-assisted keystroke inference framework to infer a tablet user's inputs from surreptitious video recordings of the tablet motion.…”

Section: Software-based Defensementioning

confidence: 99%

DolphinAttack

Zhang

Chen

et al. 2017

Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

Self Cite

499

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Speech recognition (SR) systems such as Siri or Google Now have become an increasingly popular human-computer interaction method, and have turned various systems into voice controllable systems (VCS). Prior work on attacking VCS shows that the hidden voice commands that are incomprehensible to people can control the systems. Hidden voice commands, though 'hidden', are nonetheless audible. In this work, we design a completely inaudible attack, DolphinAttack, that modulates voice commands on ultrasonic carriers (e.g., f > 20 kHz) to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated lowfrequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the speech recognition systems. We validate DolphinAttack on popular speech recognition systems, including Siri, Google Now, Samsung S Voice, Huawei HiVoice, Cortana and Alexa. By injecting a sequence of inaudible voice commands, we show a few proof-of-concept attacks, which include activating Siri to initiate a FaceTime call on iPhone, activating Google Now to switch the phone to the airplane mode, and even manipulating the navigation system in an Audi automobile. We propose hardware and software defense solutions. We validate that it is feasible to detect DolphinAttack by classifying the audios using supported vector machine (SVM), and suggest to re-design voice controllable systems to be resilient to inaudible voice command attacks.

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Section: Software-based Defensementioning

confidence: 99%

DolphinAttack

Zhang

Chen

et al. 2017

Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

Self Cite

499

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show abstract

“…The system was tested for person verification, location verification, and painter verification of artworks. However, the system is more closely related to approaches for metadata verification [7,8,23,26] as it only verifies the consistency between pairs of images and metadata and does not incorporate any textual information.…”

Section: Related Workmentioning

confidence: 99%

Multimodal news analytics using measures of cross-modal entity and context consistency

Müller-Budack

Theiner

Diering

et al. 2021

Int J Multimed Info Retr

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The World Wide Web has become a popular source to gather information and news. Multimodal information, e.g., supplement text with photographs, is typically used to convey the news more effectively or to attract attention. The photographs can be decorative, depict additional details, but might also contain misleading information. The quantification of the cross-modal consistency of entity representations can assist human assessors’ evaluation of the overall multimodal message. In some cases such measures might give hints to detect fake news, which is an increasingly important topic in today’s society. In this paper, we present a multimodal approach to quantify the entity coherence between image and text in real-world news. Named entity linking is applied to extract persons, locations, and events from news texts. Several measures are suggested to calculate the cross-modal similarity of the entities in text and photograph by exploiting state-of-the-art computer vision approaches. In contrast to previous work, our system automatically acquires example data from the Web and is applicable to real-world news. Moreover, an approach that quantifies contextual image-text relations is introduced. The feasibility is demonstrated on two datasets that cover different languages, topics, and domains.

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“…Considering timestamp manipulation, Kakar et al [15] and Li et al [19] propose to verify the timestamp by estimating the sun azimuth angle from shadow angles and sky appearance, comparing it to the sun position calculated from the image metadata. Chen et al [3] optimize a CNN to jointly estimate temperature, humidity, sun altitude angle and weather condition from an input image, comparing them with meteorological data registered from the day and time stored in its metadata.…”

Section: Metadata Tampering Detectionmentioning

confidence: 99%

“…Moreover, factors such as the weather, lighting conditions, device quality, and depicted elements influence the appearance of a recorded scene and directly affect our perception of time. Existing methods often estimate indirect features from the image content, such as the sun position in the sky [3,15,19] or meteorological measures [3], and contrast them to registered values for the same day, hour and location. However, these are limited cues which may not always be sufficient nor easily available.…”

Section: Introductionmentioning

confidence: 99%

Content-Aware Detection of Temporal Metadata Manipulation

Padilha,

Salem,

Workman

et al. 2021

Preprint

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Most pictures shared online are accompanied by a temporal context (i.e., the moment they were taken) that aids their understanding and the history behind them. Claiming that these images were captured in a different moment can be misleading and help to convey a distorted version of reality. In this work, we present the nascent problem of detecting timestamp manipulation. We propose an end-to-end approach to verify whether the purported time of capture of an image is consistent with its content and geographic location. The central idea is the use of supervised consistency verification, in which we predict the probability that the image content, capture time, and geographical location are consistent. We also include a pair of auxiliary tasks, which can be used to explain the network decision. Our approach improves upon previous work on a large benchmark dataset, increasing the classification accuracy from 59.03% to 81.07%. Finally, an ablation study highlights the importance of various components of the method, showing what types of tampering are detectable using our approach.

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Are You Lying: Validating the Time-Location of Outdoor Images

Cited by 10 publications

References 17 publications

DolphinAttack

DolphinAttack

Multimodal news analytics using measures of cross-modal entity and context consistency

Content-Aware Detection of Temporal Metadata Manipulation

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