Point of Interest (POI) search faces unique challenges in effectively combining semantic relevance, geographic context, and location priorities. To address these challenges, we propose a novel two-stage approach that leverages modern text embeddings for both semantic and geographic matching. Our method fuses the user query, user address, and POI metadata into a unified text format for dense retrieval, followed by a re-scoring stage that incorporates distance and popularity biases. Experiments on a 17 million US location database demonstrate that our dense retrieval method significantly surpasses traditional lexical approaches, and the addition of distance and popularity biases further enhances performance achieving an improvement from 55.3% to 81.1% Recall@100.

Geotagging images of interest are increasingly important to law enforcement, national security, and journalism. Today, many images do not carry location tags that are trustworthy and resilient to tampering; and landmark-based visual clues may not be readily present in every image, especially in those taken indoors. In this paper, we exploit an environmental signature from the power grid, the electric network frequency (ENF) signal, which can be inherently captured in a sensing stream at the time of recording and carries useful time–location information. Compared to the recent art of extracting ENF traces from audio and video recordings, it is very challenging to extract an ENF trace from a single image. We address this challenge by first mathematically examining the impact of the ENF embedding steps such as electricity to light conversion, scene geometry dilution of radiation, and image sensing. We then incorporate the verified parametric models of the physical embedding process into our proposed entropy minimization method. The optimized results of the entropy minimization are used for creating a two-level ENF presence–classification test for region-of-capturing localization. It identifies whether a single image has an ENF trace; if yes, whether it is at 50 or 60 Hz. We quantitatively study the relationship between the ENF strength and its detectability from a single image. This paper is the first comprehensive work to bring out a unique forensic capability of environmental traces that shed light on an image’s capturing location.

The electric network frequency (ENF) signal is an environmental signature that can be captured in audiovisual recordings made in locations where there is electrical activity. This signal is influenced by the power grid in which the recording is made, and recent work has shown that it can be useful toward a number of forensics and security applications. An under-studied area of ENF research is the factors that can affect the capture of ENF traces in media recordings. Not all recordings made in the areas of electrical activity will carry prominent ENF traces, and the strengths by which the ENF traces are captured can vary from one recording to another. A thorough understanding of the factors that can affect the capture of ENF traces in recordings is essential to understanding the applicability of ENF-based approaches and can help inform related studies in the future. This paper carried out a study on such factors, with a focus on audio signals. The impact of the characteristics of an audio recorder and the environment and manner of recording on the intrinsically captured ENF are shown and analyzed.

Geo-tagging images of interest is increasingly important to law enforcement, national security, and journalism. Many images today do not carry location tags that are trustworthy and resilient to tampering; and the landmark-based visual clues may not be readily present in every image, especially in those taken indoors. In this paper, we exploit an invisible signature from the power grid, the Electric Network Frequency (ENF) signal, which can be inherently recorded in a sensing stream at the time of capturing and carries useful location information. It is, however, very challenging to extract an ENF signal from a single image, as compared to the recent art in extracting ENF traces from audio and video. This paper presents novel investigations toward this challenge, by synergistically exploring the rolling shutter effect of CMOS imaging sensors and entropy differences of composite signals. We study quantitatively the relationship between the ENF strength and its detectability from a single image, and bring out a unique forensics capability of invisible traces that shine a light on an image's capturing location.

Camcorder piracy refers to the process of using a camcorder to record a screen that displays copyrighted content. In contrast to the previous works that aimed at detecting the occurrence of camcorder piracy, this paper conducts an in-depth study of the luminance flicker that is naturally present in camcorded videos due to the interplay between a liquid-crystal-display (LCD) screen and a camcorder. We first model the flicker signal and show that its parameters are tied to such internal characteristics of the pirate devices as the back-light frequency of the LCD screen and the read-out time of the camcorder. We then present new estimation techniques to recover these hidden parameters directly from camcorded videos and demonstrate that such forensic cues could provide intelligence on the pirate devices. We also discuss how to recover the shape of the low-power flicker signal itself and show that it could be used to infer which back-light technology employed in the pirate LCD screen.

The electric network frequency (ENF) is a signature of power distribution networks that can be captured by multimedia signals recorded near electrical activity. This has led to the emergence of multiple forensic applications based on the use of ENF signals. Examples of such applications include validating the time of recording of an ENF-containing multimedia signal or inferring the grid in which it was recorded. In this letter, we explore a novel ENF-based application that seeks to characterize the camera that has produced a given video. Inspired by recent work on exploiting flicker for pirate device identification, we investigate the use of ENF captured in a video to characterize the camera that has produced the video through a nonintrusive procedure that estimates the camera's read-out time. The proposed technique has achieved a high accuracy in estimating this discriminating parameter with a relative estimation error within 1.5%.

Cryptography-based content protection is an efficient means to protect multimedia content during transport. Nevertheless, content is eventually decrypted at rendering time, leaving it vulnerable to piracy e.g. using a camcorder to record movies displayed on an LCD screen. Such type of piracy naturally imprints a visible flicker signal in the pirate video due to the interplay between the rendering and acquisition devices. The parameters of such flicker are inherently tied to the characteristics of the pirate devices such as the back-light of the LCD screen and the read-out time of the camcorder. In this article, we introduce a forensic methodology to estimate such parameters by analyzing the flicker signal present in pirate recordings. Experimental results clearly showcase that the accuracy of these estimation techniques offers efficient means to tell-tale which devices have been used for piracy thanks to the variety of factory settings used by consumer electronics manufacturers.

The electric network frequency (ENF) is a signature of power distribution networks that can be captured by multimedia signals recorded near electrical activities. This has led to the emergence of multiple forensic applications based on the use of ENF signals. Examples of such applications include validating the time-of-recording of an ENF-containing multimedia signal or estimating its recording location based on concurrent reference signals from power grids. In this paper, we examine a novel ENF-based application that infers the power grid in which the ENF-containing multimedia signal was recorded without relying on the availability of concurrent power references. We investigate features based on the statistical differences in ENF variations between different power grids to serve as signatures for the region-of-recording of the media signal. We use these features in a multiclass machine learning implementation that is able to identify the grid-of-recording of a signal with high accuracy. In addition, we explore techniques for building multiconditional learning systems that can adapt to changes in the noise environment between the training and testing data.

The electric network frequency (ENF) signal can be captured in multimedia recordings due to electromagnetic influences from the power grid at the time of recording. Recent work has exploited the ENF signals for forensic applications, such as authenticating and detecting forgery of ENF-containing multimedia signals, and inferring their time and location of creation. In this paper, we explore a new potential of ENF signals for automatic synchronization of audio and video. The ENF signal as a time-varying random process can be used as a timing fingerprint of multimedia signals. Synchronization of audio and video recordings can be achieved by aligning their embedded ENF signals. We demonstrate the proposed scheme with two applications: multi-view video synchronization and synchronization of historical audio recordings. The experimental results show the ENF based synchronization approach is effective, and has the potential to solve problems that are intractable by other existing methods.

The electric network frequency (ENF) signal can be embedded in multimedia recordings created in areas of electrical activities. Recent work has used the ENF signal for such applications as time stamp authentication and forgery detection. It is more challenging to extract ENF signals from video recordings than from audio recordings because of the low temporal sampling rate or frame rate of video cameras. The rolling shutter of CMOS image sensor can be exploited as it exposes a frame line by line, and the effective ENF sampling rate by treating each line as a signal sample can be increased. This scheme was shown to work well with static videos. This paper conducts a further study on the exploitation of the rolling shutter for extracting ENF traces from videos. The rolling shutter mechanism is modeled and analyzed using multirate signal processing theory. Challenging cases of videos with motions are examined, and solutions to extracting ENF from them are explored.

Electric Network Frequency (ENF) fluctuations based forensic analysis is an emerging way for such multimedia authentication tasks as time-of-recording estimation, timestamp verification, and clip insertion/deletion forgery detection. ENF fluctuates due to dynamic changes in load demand and power supply, and these fluctuations travel over the power lines with a finite speed. In this paper, experiments are conducted on ENF data collected across different locations in the eastern grid of the United States to understand the relationship between the signals recorded at the same time at these locations. Based on these experiments, a signal processing mechanism is developed to demonstrate that ENF fluctuations across different locations exhibit a measure of similarity with each other, which is proportional to the distance between the locations. Such observations motivated a location estimation protocol based on the similarity of ENF signals with respect to anchor nodes. Under certain conditions, the proposed protocol is shown to provide an estimation accuracy of 90%. Challenges in the application of ENF signal analysis for location of recording estimation of multimedia signal are also discussed.

Electric Network Frequency (ENF) based forensic analysis is a promising tool for timestamp authentication and forgery detection in such multimedia recordings as audios and videos. ENF signal is embedded in an audio recording due to electromagnetic interference from the power lines. The time of creation of a multimedia recording can be determined by comparing the ENF signal embedded in the recording with a reference ENF database collected from the power grid. In this paper, we conduct a study of the effect of recapturing of audio recordings on the ENF embedding. We demonstrate that recaptured audio recordings pick up two ENF signals: the content ENF signal which is inherited from the original audio recording; and the recapturing ENF signal which is embedded from the recapturing process. Conventional ENF signal extraction techniques on such recordings may fail when the two ENF signals are at the same nominal value. A decorrelation algorithm is proposed to extract the content ENF signal and the recapturing ENF signal. The experimental results show the effectiveness of the proposed method in the estimation of both the ENF signals.

The Electric Network Frequency (ENF) is the supply frequency of power distribution networks, and is often captured by audio or video measurements recorded near power supplies. The time varying nature of the ENF allows it to be used for such forensic applications as estimating the time and location of media recordings, and discerning their integrity. An initial step in such applications is to extract the ENF signal-instantaneous ENF values over time-as accurately as possible. Existing techniques rely on estimating the ENF around the nominal frequency of 50/60 Hz, or around one of its harmonics at a time. In this letter, a novel spectrum combining approach is proposed, which exploits the presence of the ENF around different harmonics of the nominal frequency. The ENF signal is estimated by combining the ENF at multiple harmonics, based on the local signal-to-noise ratio at each harmonic. A hypothesis testing performance of an ENF-based timestamp verification application is examined to validate that the proposed approach achieves a more robust and accurate performance than conventional ENF estimation techniques.

Electric Network Frequency (ENF) fluctuates slightly over time from its nominal value of 50 Hz/60 Hz. The fluctuations in ENF remain consistent across the entire power grid including when measured at physically distant geographical locations. The light intensity from such indoor lighting as fluorescent lamps and incandescent bulbs, which are connected to the power mains, varies in accordance with the ENF, and the fluctuations can be recorded using visual sensors. In this paper, mechanisms using optical sensors and video cameras to record and validate the presence of ENF fluctuations in indoor lighting are presented. Spectrogram and subspace-based signal processing techniques are applied to such recordings to extract the ENF signal by estimating its instantaneous frequencies as a function of time. A high correlation is observed between the ENF fluctuations obtained from indoor lighting and that of the ENF signal captured directly from the power mains supply. A similar mechanism is then used to demonstrate the presence of ENF signals in video recordings taken in different geographical areas. Experimental results show that ENF signals are present in visual recordings made in different geographical areas and can be used as a natural timestamp for optical sensor recordings and video surveillance recordings conducted in indoor lighting environments. Robustness of ENF fluctuation traces under strong compression and CMOS rolling shutter cameras is discussed. Applications of the ENF signal analysis to tampering detection of surveillance video recordings and forensic binding of the audio and visual track of a video are also demonstrated. An analytical model based on an autoregressive process is also developed for ENF signals, and the effectiveness of using innovation sequences from the model for timestamp verification is demonstrated.